U.S. patent number RE38,626 [Application Number 10/177,061] was granted by the patent office on 2004-10-19 for parking regulation enforcement system.
This patent grant is currently assigned to Visionary Technology, Inc.. Invention is credited to Peter J. Kielland.
United States Patent |
RE38,626 |
Kielland |
October 19, 2004 |
Parking regulation enforcement system
Abstract
A video camera mounted on a parking enforcement patrol vehicle
and connected to a computer near the operator. The system is driven
along a patrol route where parked vehicles are governed by a posted
time limit. The system enforces the local parking regulation by
automatically determining whether or not each parked car has been
parked longer than the posted time limit. Violations are detected
by applying a License Plate Recognition algorithm to the images.
Each license plate number is time-tagged, geo-referenced and
entered into a local database. When the patrol vehicle re-traces
the patrol route after the posted parking time limit has expired,
the database is searched to flag vehicles that were observed at the
same location during the previous circuit and therefore in
violation of the parking regulations. When the system detects a
parking violation, it prints a parking citation that the operator
affixes to the offending parked vehicle.
Inventors: |
Kielland; Peter J. (Ottawa,
CA) |
Assignee: |
Visionary Technology, Inc.
(Ontario, CA)
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Family
ID: |
33135380 |
Appl.
No.: |
10/177,061 |
Filed: |
June 21, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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Reissue of: |
036159 |
Mar 6, 1998 |
06081206 |
Jun 27, 2000 |
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Foreign Application Priority Data
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Mar 14, 1997 [CA] |
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2199999 |
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Current U.S.
Class: |
340/937; 194/902;
235/384; 340/425.5; 340/932.2; 348/104; 348/148; 348/159; 382/104;
705/13; 705/418 |
Current CPC
Class: |
G07B
15/02 (20130101) |
Current International
Class: |
G07B
15/02 (20060101); G08G 001/017 (); B60Q
001/48 () |
Field of
Search: |
;340/937,932.2,425.5,555,556 ;382/104,105 ;348/148,149,135,143,159
;235/384 ;705/13,418 ;194/900,902,207 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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4401993 |
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Jul 1995 |
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DE |
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0367725 |
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May 1990 |
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EP |
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2226904 |
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Nov 1974 |
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FR |
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193320 |
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Feb 1986 |
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GB |
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2265243 |
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Sep 1993 |
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GB |
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2279478 |
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Jan 1995 |
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GB |
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2284290 |
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May 1995 |
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GB |
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08242442 |
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Sep 1996 |
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JP |
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WO 94/08820 |
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Apr 1994 |
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WO |
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WO9714116 |
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Apr 1997 |
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WO |
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Primary Examiner: Crosland; Donnie L.
Attorney, Agent or Firm: Pratt; John S. Gavin; Geoffrey K.
Kilpatrick Stockton LLP
Claims
What is claimed is:
1. A parking regulation enforcement system for monitoring a parked
vehicle, the system comprising: a camera capturing a first image of
the parked vehicle at a first observation time and a second image
of the vehicle at a second observation time; a data processing
sub-system coupled to the camera, the data processing sub-system
extracting an identifier of the parked vehicle from the first image
and the second image; means within the data processing sub-system,
for determining a first position of a predetermined measurement
point at the first observation time and a second position of the
measurement point at the second observation time, the means
comprising a global positioning system; a memory within the data
processing sub-system, the memory storing a first data record, the
first data record comprising the identifier, the first observation
time, and the first position of the measurement point; and means,
within the data processing sub-system, for comparing the first data
record to a second data record, the second data record including
the identifier, the second observation time, and the second
position of the measurement point, and for generating an
output.
2. The parking regulation enforcement system, as recited in claim
1, further comprising: a proximity sensor coupled to the data
processing sub-system, the sensor measuring a distance between the
parked vehicle and the measurement point; means for triggering the
camera to capture the first and second images when the measured
distance is equal to a predetermined value.
3. The parking regulation enforcement system, as recited in claim
1, further comprising: a metal detector coupled to the camera;
means for triggering the camera to capture the first and second
images when the metal detector detects a pre-determined amount of
metal.
4. The parking regulation enforcement system, as recited in claim
1, wherein the identifier is a license plate number of the parked
vehicle.
5. The parking regulation enforcement system, as recited in claim
4, wherein the sub-system uses a pattern-matching license plate
recognition algorithm to extract the license plate number from the
first image and the second image.
6. The parking regulation enforcement system, a recited in a claim
4, wherein the sub-system uses a full recognition mode license
plate recognition algorithm to extract the license plate number
from the first image and the second image.
7. The parking regulation enforcement system, as recited in claim
4, wherein the sub-system uses a pattern matching license plate
recognition algorithm and a full recognition mode license plate
algorithm to extract the license plate number from the first image
and the second image.
8. The parking regulation enforcement system, as recited to claim
1, further comprising a radio frequency transponder coupled to the
parked vehicle, the transponder broadcasting the identifier when
interrogated.
9. The parking regulation enforcement system, as recited in claim
1, further comprising a display coupled to the camera, the display
showing the first image and the second image.
10. The parking regulation enforcement system, as recited in claim
1, wherein the camera is mounted on a second vehicle.
11. The parking regulation enforcement system, as recited in claim
10, wherein the measurement point is a location on the second
vehicle.
12. A system for locating a lost vehicle comprising: a sensor
capturing an image of the lost vehicle at an observation time; a
sub-system, within a data processing system coupled to the sensor,
the sub-system extracting an identifier of the lost vehicle from
the image; a positioning system, coupled to the data processing
system, for determining a position of a predetermined measurement
point at the observation of a predetermined measurement point at
the observation time; a concatenation system for concatenating the
identifier, the observation time, and the position of the
measurement point into a vehicle data record; a display device
coupled to the data processing system for displaying a location of
the lost vehicle on the display device; a database coupled to the
data processing system, the database storing a plurality of data
records, each data record including a vehicle identifier, the
observation time, and a vehicle position; and a search engine
coupled to the database for enabling a user to traverse the
database to locate a second data record having the identifier of
the lost vehicle, to assist in identifying the lost vehicle.
13. A parking regulation enforcement system for monitoring a parked
vehicle, the system comprising: a camera capturing an image of a
vehicle at a first observation time; a data processing sub-system
coupled to the camera, the data processing sub-system extracting an
identifier of the vehicle from the image; means, within the data
processing sub-system, for determining a position of a
predetermined measurement point at the first observation time;
means for concatenating the identifier, the first observation time,
and the position of the measurement point into a vehicle data
record; a database coupled to the data processing sub-system, the
database storing a plurality of data records, each data record
including a vehicle identifier, an observation time and a vehicle
position; means for traversing the database to locate a second data
record having the identifier of the vehicle; and a computerized
billing system for receiving data, and for creating an output.
14. The parking regulation enforcement system, as recited in claim
13, further comprising: first means for comparing the observation
time in the first data record to the observation time of the second
data record; and second means for comparing the position in the
first data record to the position in the second data record.
15. The parking regulation enforcement system, as recited in claim
14, further comprising third means for comparing a difference
between the observation time in the first data record and the
observation time of the second data record to a predetermined
parking time limit.
16. The parking regulation enforcement system, as recited in claim
15, further comprising the computerized billing system, the billing
system receiving data from the first and second comparing
means.
17. The parking regulation enforcement system, as recited in claim
16, wherein the computerized billing system measures an overall
parking time of the vehicle over a predetermined period of
time.
18. The parking regulation enforcement system, as recited in claim
16, wherein the computerized billing system initializing a virtual
parking meter in the database, the virtual parking meter comparing
a plurality of data records for a particular vehicle.
19. The parking regulation enforcement system, as recited in claim
16, wherein the computerized billing system charges to fee to an
owner of the vehicle based upon a fee structure.
20. The parking regulation enforcement system, as recited in claim
19, wherein the fee structure is based on a changing hourly
rate.
21. The parking regulation enforcement system, as recited in claim
13, further comprising: a parking facility system for determining a
parking fee within a parking facility, the system determining a
parking fee by comparing the observation time in the first data
record to the observation time in the second data record.
22. The parking regulation enforcement system, as recited in claim
21, further comprising an automated gate coupled to the parking
facility system that blocks movement of the vehicle based upon data
received from the parking facility system.
23. The parking regulation enforcement system, as recited in claim
13, further comprising a second database storing geographically
referenced information.
24. The parking regulation enforcement system, as recited in claim
23, further comprising: a navigation system that records and
updates a plurality of citations for vehicles based on the
plurality of stored data records; means for estimating a time for
patrolling a route based on the plurality of citations and
geographic information to estimate the time.
25. The parking regulation enforcement system, as recited in claim
24, further comprising a display displaying information stored in
the second database.
26. The parking regulation enforcement system, as recited in claim
13, further comprising a clock within the data processing
sub-system.
27. The parking regulation enforcement system, as recited in claim
13, wherein the identifier is a license plate number of the
vehicle.
28. The parking regulation enforcement system, as recited in claim
27, wherein the sub-system uses a license plate recognition
algorithm to extract the license plate number from the image.
29. The parking regulation enforcement system, as recited in claim
28, further comprising means for comparing a result of the license
plate recognition algorithm to a predetermined threshold.
30. The parking regulation enforcement system, as recited in claim
28, wherein the vehicle data record further includes the raster
image.
31. The parking regulation enforcement system, as recited in claim
13, further comprising means for estimating an error in the
position of the vehicle, the estimated error being stored in the
vehicle data record.
32. The parking regulation enforcement system, as recited in claim
13, further comprising: a second database storing a plurality of
vehicle identifiers; means for traversing the second database to
locate one of the vehicle identifiers matching the identifier in
the vehicle data record; means for notifying a user of a located
match between the vehicle identifier and the identifier in the
vehicle data record.
33. The parking regulation enforcement system, as recited in claim
13, further comprising means for encrypting the vehicle data
record.
34. The parking regulation enforcement system, as recited in claim
13, wherein the camera captures a plurality of successive images of
the vehicle.
35. The parking regulation enforcement system, as recited in claim
13, further comprising a second camera capturing an image of a
second vehicle.
36. A system for locating a lost vehicle comprising: a camera
capturing an image of the lost vehicle at an observation time; a
sub-system, within a data processing system coupled to the camera,
the sub-system extracting an identifier of the lost vehicle from
the image; means, coupled to the data processing system, for
determining a position of a predetermined measurement point at the
observation time; means for concatenating the identifier, the
observation time, and the position of the measurement point into a
vehicle data record; a database coupled to the data processing
system, the database storing a plurality of data records, each data
record including a vehicle identifier, the observation time, and a
vehicle position; and means for enabling a user to traverse the
database to locate a second data record having the identifier of
the lost vehicle, to assist in identifying the lost vehicle.
37. The system for locating a lost vehicle as recited in claim 36,
further comprising: a display device coupled to the data processing
system; and means for plotting a location of the lost vehicle on
the display device.
38. The system for locating a lost vehicle, as recited in claim 36,
wherein the enabling means is mobile.
39. A method for enforcing parking regulations comprising:
capturing an image of a parked vehicle at a first observation time;
extracting an identifier of the parked vehicle from the image;
determining a position of a predetermined measurement point at the
first observation time; concatenating the identifier, the first
observation time, and the position of the measurement point into a
vehicle data record; storing a plurality of data records, each data
record including a vehicle identifier, an observation time, and a
vehicle position; traversing the database to locate a second data
record having the identifier of the parked vehicle; and billing a
customer based upon a billing criteria and the stored data
records.
40. The method for enforcing parking regulations, as recited in
claim 39, further comprising the steps of: comparing the
observation time in the first data record to the observation time
of the second data record; and comparing the position in the first
data record to the position and the second data record.
41. A parking regulation enforcement system for monitoring a parked
vehicle, the system comprising: a sensor for capturing a first
image of the parked vehicle at a first observation time and a
second image of the vehicle at a second observation time; a data
processing sub-system coupled to the sensor, the data processing
sub-system extracting an identifier of the parked vehicle from the
first image and the second image; a global positioning system
within the data processing sub-system, for determining a first
position of a predetermined measurement point at the first
observation time and a second position of the measurement point at
the second observation time; a memory within the data processing
sub-system, the memory storing a first data record, the first data
record comprising the identifier, the first observation time, and
the first position of the measurement point; and a comparator
within the data processing sub-system, for comparing the first data
record to a second data record, the second data record including
the identifier, the second observation time, and the second
position of the measurement point, and for generating an
output.
42. A parking regulation enforcement system for monitoring a parked
vehicle, the system comprising: a sensor for capturing an image of
a vehicle at a first observation time; a data processing sub-system
coupled to the sensor, the data processing sub-system extracting
the identifier of the vehicle from the image; a positioning
sub-system within the data processing sub-system, for determining a
position of a predetermined measurement point at the first
observation time; a concentration system for concatenating the
identifier, the first observation time, and the position of the
measurement point into a vehicle data record; a database coupled to
the data processing sub-system, the database storing a plurality of
data records, each data record including a vehicle identifier, an
observation time and a vehicle position; a search engine coupled to
the database for traversing the database to locate a second data
record having the identifier of the vehicle; and a computerized
billing system for receiving data, and for creating an output.
.Iadd.
43. A system for locating a lost vehicle comprising: a sensor
capturing an image of the lost vehicle; a sub-system within a data
processing system coupled to the sensor, the sub-system extracting
an identifier of the lost vehicle from the image; a database
coupled to the data processing system, the database storing a
plurality of data records, each data record including a vehicle
identifier; and a search engine coupled to the database for
enabling a user to traverse the database to locate a second data
record having the identifier of the lost vehicle, to assist in
identifying the lost vehicle..Iaddend..Iadd.
44. The system for locating a lost vehicle as recited in claim 43,
wherein the sensor captures the image of the lost vehicle at an
observation time..Iaddend..Iadd.
45. The system for locating a lost vehicle as recited in claim 44,
further comprising a positioning system, coupled to the data
processing system, for determining a position of a predetermined
measurement point at the observation time..Iaddend..Iadd.
46. The system for locating a lost vehicle as recited in claim 45,
further comprising a concatenation system for concatenating the
identifier, the observation time, and the position of the
measurement point into a vehicle data record..Iaddend..Iadd.
47. The system for locating a lost vehicle as recited in claim 46,
wherein each data record further includes an observation time and a
vehicle position..Iaddend..Iadd.
48. The system for locating a lost vehicle as recited in claim 43,
further comprising a display device coupled to the data processing
system for displaying a location of the lost vehicle on the display
device..Iaddend..Iadd.
49. A system for locating a lost vehicle comprising: a camera
capturing an image of the lost vehicle; a sub-system within a data
processing system coupled to the camera, the sub-system extracting
an identifier of the lost vehicle from the image; a database
coupled to the data processing system, the database storing a
plurality of data records, each data record including a vehicle
identifier; and means for enabling the user to traverse the
database to locate a second data record having the identifier of
the lost vehicle, to assist in identifying the lost
vehicle..Iaddend..Iadd.
50. The system for locating a lost vehicle as recited in claim 49,
wherein the camera captures the image of the lost vehicle at an
observation time..Iaddend..Iadd.
51. The system for locating a lost vehicle as recited in claim 50,
further comprising means, coupled to the data processing system,
for determining a position of a predetermined measurement point at
the observation time..Iaddend..Iadd.
52. The system for locating a lost vehicle as recited in claim 51,
further comprising means for concatenating the identifier, the
observation time, and the position of the measurement point into a
vehicle data record..Iaddend..Iadd.
53. The system for locating a lost vehicle as recited in claim 49,
further comprising a display device coupled to the data processing
system for displaying a location of the lost vehicle on the display
device..Iaddend..Iadd.
54. A system for locating a lost vehicle comprising: a first radio
frequency transponder for receiving a transmission from a second
radio frequency transponder coupled to a lost vehicle, the second
transponder broadcasting an identifier when interrogated; a
sub-system within a data processing system coupled to the first
transponder, the sub-system extracting the identifier of the lost
vehicle; database coupled to the data processing system, the
database storing a plurality of data records, each data record
including a vehicle identifier; and a search engine coupled to the
database for enabling a user to traverse the database to locate a
second data record having the identifier of the lost vehicle, to
assist in identifying the lost vehicle..Iaddend..Iadd.
55. The system for locating a lost vehicle as recited in claim 54,
wherein the first transponder receives the transmission at an
observation time..Iaddend..Iadd.
56. The system for locating a lost vehicle as recited in claim 55,
further comprising a positioning system, coupled to the data
processing system, for determining a position of a predetermined
measurement point at the observation time..Iaddend..Iadd.
57. The system for locating a lost vehicle as recited in claim 56,
further comprising a concatenation system for concatenating the
identifier, the observation time, and the position of the
measurement point into a vehicle data record..Iaddend..Iadd.
58. The system for locating a lost vehicle as recited in claim 57,
wherein each data record further includes an observation time and a
vehicle position..Iaddend..Iadd.
59. The system for locating a lost vehicle as recited in claim 54,
further comprising a display device coupled to the data processing
system for displaying a location of the lost vehicle on the display
device..Iaddend..Iadd.
60. The system for locating a lost vehicle as recited in claim 54,
further comprising a sensor capturing an image of the lost
vehicle..Iaddend..Iadd.
61. The system for locating a lost vehicle as recited in claim 60,
further comprising the sensor coupled to the sub-system, the
sub-system extracting a second identifier of the lost vehicle from
the image..Iaddend..Iadd.
62. The system for locating a lost vehicle as recited in claim 43,
wherein a lost vehicle is a vehicle that is: reported stolen;
registered to an owner with a suspended driver's license;
registered to an owner with an unpaid public debt; registered to an
owner who is a wanted fugitive; registered to an owner who is in
violation of the owner's parole conditions; subject to outstanding
liens; or wanted by a law enforcement or public service
agency..Iaddend..Iadd.
63. The system for locating a lost vehicle as recited in claim 49,
wherein a lost vehicle is a vehicle that is: reported stolen;
registered to an owner with a suspended driver's license;
registered to an owner with an unpaid public debt; registered to an
owner who is a wanted fugitive; registered to an owner who is in
violation of the owner's parole conditions subject to an
outstanding liens; or wanted by a law enforcement or public service
agency..Iaddend..Iadd.
64. The system for locating a lost vehicle as recited in claim 54,
wherein a lost vehicle is a vehicle that is: reported stolen;
registered to an owner with a suspended driver's license;
registered to an owner with an unpaid public debt; registered to an
owner who is a wanted fugitive; registered to an owner who is in
violation of the owner's parole conditions; subject to outstanding
liens; or wanted by a law enforcement or public service
agency..Iaddend.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to law enforcement and more particularly to
an automated means for detecting vehicles that have been parked for
longer than the legally prescribed period.
2. Background
Municipal governments enact regulations to govern the parking of
cars along city streets. Typically, time limits are posted along
each street and parking fines are levied on vehicle owners who park
their cars for longer than the posted time. Two benefits result
from the practice of making and enforcing on-street parking
regulations:
1) Traffic congestion is reduced by forcing motorists parked for
long periods to find suitable off-street parking arrangements,
thereby vacating their more convenient, on-street parking spaces
for use by motorists wishing to stop for short periods.
2) The parking fines levied on motorists who violate parking
regulations create revenue the municipality.
In order to reap these benefits, the fundamental technical problem
faced by Parking Authorities is how to detect when vehicles are in
violation for the posted time limit. Heretofore, two
violation-detection and enforcement technologies have been
employed: 1) Parking meters 2) Timed chalk-marking of car tires
Enforcement Using Parking Meters
Parking meters are timing devices installed adjacent to each
parking space that the Parking Authority wishes to enforce. Once
installed, parking meters permit motorists to rent each on-street
parking space for short periods. To rent the space, the motorist
must insert coins into the meter, thereby starting a timer
mechanism that suppresses display of an "Illegally Parked" flag.
When the purchased parking period has expired, the "Illegally
Parked" flag is again made plainly visible, thereby enabling a
Parking Enforcement Officer patrolling the area to see at a glance
that the parking space is illegally occupied. The officer
continually inspects every parking meter along the patrol route and
issues citations to those cars that are illegally parked.
Detecting parking violations with parking meters is an effective
means of enforcing regulations, particularly in areas with high
traffic density such as downtown commercial districts. A
significant advantage of using parking meters to detect infractions
is that they also provide a means for collecting a "pay per use"
rental fee. The requirement to insert coins provides a continual
stream of revenue to the municipality, even if no vehicle is ever
cited for an over-parking infraction. However, each parking space
requires its own parking meter, which is an expensive piece of
equipment to purchase and install. The capital costs of initiating
a parking metered enforcement program are considerable. Since the
Enforcement Officer must visually inspect each parking meter along
the route, patrolling the meters is a tedious, labour intensive
activity that adds to the overall cost of metered enforcement. In
congested, downstream areas, officers are often obliged to patrol
the route on foot, thereby adding to the labor cost of the system.
Maintaining the meters in good working order and emptying their
contents is another significant expense related to metered
enforcement.
Enforcement Using Timed Chalk-Marking of Car Tires
The high cost of installing, maintaining and patrolling parking
meters limits their cost-effectiveness in many on-street parking
situations. In particular, low-density areas outside the downtown
core may be considered "not profitable enough" to warrant the use
of parking meters. In these areas, the other method of parking
enforcement commonly employed is "timed chalk-marking of car tires"
(hereinafter referred to as "tire-chalking").
Parking regulation enforcement using the tire-chalking methodology
is as follows:
1) A route is chosen such that all the parked cars along it are
subject to the same parking regulation (e.g. 2-hour parking limit).
The Officer patrols the route and stops beside every parked car
that's encountered. Typically, the patrol is done using a car
however foot and bicycle patrols are also common modes of
transportation.
2) A temporary mark is made on one of each car's tires using a
piece of chalk or similar marking utensil. In order that the
officer can attest to having made the mark, some effort is made to
keep all the marks similar in size, color, shape and placement.
3) At regular intervals along the route, the time is noted, thereby
the enabling a time to be estimated for when each of the
chalk-marks was made.
4) After all of the cars parked along the patrol route have been
marked, the officer retraces the same route. Care is taken to
regulate the speed of the patrol such that the officer returns to
the location of each of the chalk-marks just after the permissible
parking period has expired (e.g., if the posted time limit is two
hours, then the officer must return to the same location slightly
more than two hours after chalk marks were made at that
location).
5) During the second trip over the patrol route, the officer
visually inspects the tires of each and every vehicle looking for a
chalk-mark made during the previous circuit. A found chalk-mark
serves as evidence that the marked vehicle has not moved during the
period the Officer has been away patrolling the rest of the
circuit.
6) When a chalk-marked car (i.e. an illegally parked car) is
sighted the officer issues it a parking citation. After writing the
details of the infraction onto the citation and attaching it to the
offending vehicle, the Officer continues along the route, slowing
down or speeding up as necessary to stay on-schedule for detecting
subsequent parking violations.
The chalk-mark method of detecting parking violations is commonly
used along lightly traveled streets where metered enforcement would
not be cost-effective. Since no capital investment in parking
meters is required to provide infrastructure, a tire-chalking
enforcement program is less costly to initiate than an enforcement
program based on parking meters.
Furthermore, tire-chalking provides a more flexible means of
parking enforcement. Patrol routes can be quickly adapted to suite
the changing parking habits that generally occur at different times
of the day, on different days of the week or in different seasons
of the year; something that meters cannot easily accommodate.
While the capital cost of using chalk-marks as a means to enforce
parking regulations is less than that of using parking meters, the
labor cost of using chalk-mark detection is significantly higher.
The principal factor contributing to the workload is the need to
manually mark every car along the patrol route . . . a task that is
both physically demanding and time consuming.
Furthermore, the route must be patrolled twice before any
infractions can be detected whereas parking meters guide the
Officer to infractions every time the route is patrolled. The high
labour cost of first applying chalk-marks and then searching for
them significantly reduces this methodology's attractiveness as a
parking enforcement means. Furthermore, the second traverse of the
patrol route is often dedicated only to the inspecting tires and
issuing citations, thereby permitting newly parked vehicles to go
unmarked.
Furthermore, detection and prosecution is based entirely on the
presence of chalk-marks on each vehicle. Vehicle owners can evade
prosecution simply by hiding the mark. Typically, each tire is
marked on its tread surface so simply moving the car a few feet
within the parking space will rotate it away from the officer's
view, thereby making it impossible to detect the infraction during
the second traverse of the patrol route. If the chalk-mark has been
made on the side of the tire rather than on its tread, the mark can
still be easily rubber off to evade detection.
Regardless of whether parking regulations are enforced using
parking meters or tire-chalking, once a parking infraction is
detected, creating a legal citation and serving it on the vehicle's
owner takes a considerable amount of time and effort. The main
factor contributing to this workload is the requirement for the
officer to write down all the details of the infraction by hand
onto a paper citation from before affixing it to the offending
vehicle (time, location, license plate number, nature of
infraction, etc). Furthermore, the labour cost of processing each
parking citation is increased by the requirement to transcribe the
hand-written data into a computerized system that tracks the
infraction through the court system.
Another factor that degrades the performance of both enforcement
systems is their incapacity to detect "scofflaw" motorists.
"Scofflaw" is the term commonly used by Parking Authorities for a
motorist who flouts parking regulations. Scofflaws flour parking
regulations by discarding or otherwise ignoring all parking
citations they receive. Neither the parking meter enforcement
methodology nor the tire-chalking enforcement methodologies can
detect whether or not the vehicle's owner is likely to pay the fine
levied for the infraction. Since many of the citations written by
officers are ignored by scofflaw motorists, the inability of both
the meter and chalk-mark enforcement methodologies to deal
effectively with scofflaw motorists reduces their fiscal
efficiency.
It is therefore the purpose of the present invention to provide a
means of enforcing parking regulations that eliminates the
drawbacks inherent to using either parking meters or
tire-chalking.
LPR Technical Background
The present invention exploits "Optical Character Recognition". OCR
image analysis is a well-established technology that has many
applications in the publishing and archiving industry. Essentially,
OCR is an image analysis process that converts a raster-scanned
image of printed characters into machine readable ASCII codes,
thereby eliminating the need to re-type old documents into a
computer and rendering them amenable to automated processing.
One common application of OCR technology is to digitize a vehicle's
license plate number from its raster image. When applied to
vehicular imagery, OCR technology is commonly referred to as
"License Plate Recognition" (LPR). Heretofore, LPR has been applied
to stationary law enforcement and security applications (e.g.
identifying vehicles in controlled areas such as parking garages).
LPR technology has also been successfully applied in revenue
collection applications (e.g. automatic billing of motorists using
toll highways).
LPR is a complex process that is well documented in the literature
and prior art. Various aspects of LPR methodology and terminology
are relevant to the present invention and therefore merit summary
description.
Essentially, LPR is comprised of three operations that are
sequentially applied to the vehicle's raster image. These processes
attempt to progressively refine the complex, unique identification
of the vehicle captured in the raster image into an alphanumeric
string of text identical to the text inscribed on the vehicle's
license plate. Since this alphanumeric string of test is compact,
easily comprehended and legally linked to the vehicle's owner, its
correct extraction from the raster image is the ultimate goal of
LPR. The interim digital encapsulations of the raster image that
are part of the LPR process are less desirable however they also
uniquely identify the vehicle in a way that has been exploited in
certain LPR applications. The interim encapsulations of LPR are
analogous to a person's fingerprint while the end product of LPR
(the license plate number) is analogous to the same person's
name.
The three conceptual steps that comprise LPR are: Vectorizing the
raster image (hereafter referred to as creating the "vector-model")
Step 1)Isolating only those vectors that describe the license plate
within the vector-model (hereafter referred to as creating the
"plate-model") Step 2)Recognizing the alphanumeric characters in
the plate-model (hereafter referred to as creating the
"plate-string")
The three steps that comprise LPR can be summarized as follows:
Step 1) Vectorizing The Raster Image:
Discrete physical objects depicted in a raster image will generate
zones within which all the pixels share similar color or gray-scale
values. Vectors are mathematically defined lines that trace the
perimeter of these zones. Some LPR algorithms make use of the
aggregation of pixels inside these zones rather than their
perimeter however for the purpose of this summary, they can be
considered the same geometric entities. Before tracing the outline
of these zones, spatial filtering algorithms are applied to the
raster image to compensate for the effects of extraneous pixel
noise (such as varying color caused by precipitation, dirt on the
vehicle, slight variations in paint color, etc). The object of
vectorization is to identify and group only those pixels that
correspond to real physical objects portrayed as discrete visual
features in the raster image. In the case of a parked car's raster
image, the desired vectors follow the silhouettes of the various
mechanical parts and visual features that comprise the car
(windows, fenders, bumpers, license plate, license plate text, dirt
on license plate, etc). The vectorization algorithm may also
outline discrete elements in the visible background scenery
(sidewalk, trees, pedestrians etc.).
Spurious shadows in the vehicle's raster image will degrade the
spat ial fidelity of vectors extracted from it. Therefore, many LPR
systems improve their performance by illuminating the scene with
supplementary lights, to minimize shadow effects in the image
presented to the vectorization algorithm.
The set of all vectors extracted from a raster image using a
particular algorithm constitutes a unique "digital fingerprint" for
the scene in the ima ge. This unique identifier is hereafter
referred to as the image's "vector-model". A vector-model generally
occupies less storage space than the raster image from which it is
derived. In addition, since the points and lines in the
vector-model are mathematically defined entities, they lend
themselves to the rapid computations required in steps 2 and 3
described below.
Step 2) Recognizing the License Plate Within the Vector-Model:
Algorithms are then applied to the image's vector-model to isolate
only those vectors or zones of similar pixels that describe the
license plate's physical structure. This unique "digital
fingerprint" of the license plate is hereafter referred to as the
"plate-model". Different algorithms could be applied to the
vector-model to try to isolate other physical structures (the
"bumper-model" the "window-model" etc). However, for typical
applications, the license plate is the physical object of greatest
interest, therefore the plate-model is the subset searched for
within the image.
The rectangular shape of a license plate provides one criterion for
testing if a candidate subset set of vectors is indeed the
plate-model. However, there will typically be many vectorized
rectangles in the vector-model that complicate isolating the
plate-model (dealer logos, bumper stickers, parking permits,
decorative trim etc.). Therefore, multiple geometric and stochastic
tests are typically made on all candidate plate-models in order to
rank their probability of being the correct one. When one of the
candidate plate-models achieves a sufficiently high probability of
modeling the real license plate, it is passed on to step 3
(described below).
Some LPR implementations only vectorize a subset of the total
raster image and create the plate-model directly. Various methods
have been used to directly localize the plate. One approach is to
exploit the reflective paint used on many license plates. The
plate's reflective surface can be used to localize it within the
image without the need to vectorize other physical elements in the
scene. Different LPR manufacturers use different terminology for
the image's interim states as it is prepared for recognition of the
license plate's alphanumeric characters. For the purposes of the
present invention, the end product of LPR (the license plate
number) as well as its precursor stages (referred to here as the
raster image, the vector-model and the plate-model) are all
encompassed within the term "unique vehicle identifier".
Step 3) Recognizing the Alphanumeric Characters in the
Plate-Model:
The plate-model is then analyzed to transform the vectorized zones
within its perimeter into an alphanumeric string of characters that
spell out the vehicle's license plate number. The recognized string
of text that estimates the vehicle's license plate number is
hereafter referred to as the "plate-string".
Typically, before attempting to recognize the plate-string's
characters, the distortion caused by an oblique camera angle is
geometrically rectified. This geometric rectification procedure is
generally referred to as "de-skewing". Since character recognition
is based on analysis of the plate-model's geometry, de-skewing the
perspective distortion of the vectorized zones will improve the
accuracy of the character recognition algorithm.
Typically, one of three OCR methodologies is used to recognize each
character of the plate-string from within the plate-model.
"Structural analysis", "pattern matching", and "neural networks"
are the terms commonly used for these algorithms. Each of these
complex algorithms is well documented in the literature and has its
unique advantages and disadvantages. Some LPR systems use
combinations thereof to improve the reliability of the characters
recognized from the plate-model.
To improve the reliability of character recognition, the LPR
algorithm must also be customized to accommodate the different
fonts, color schemes and character syntax's appearing on the plates
issued in different transportation jurisdictions.
Full Recognition Mode LPR
The sequential 3-step algorithm described above is commonly known
as "Full Recognition Mode LPR". Full Recognition Mode LPR
algorithms cannot recognize the plate-strings of all observed
vehicles with 100 percent accuracy. However, for some applications
a certain number of plate-string errors is acceptable. For example,
it is acceptable that a certain percentage of vehicles passing
through a toll plaza not be correctly recognized (and thereby
escape being billed the toll charge). Unrecognizable plates can be
tolerated if the algorithm is at least able to compute that its
best estimate of the plate-string is not sufficiently reliable,
thereby permitting the enforcement system to simply ignore those
"difficult" plate readings.
Pattern-Matching LPR
Some other LPR applications demand a very high degree of certainty
that certain vehicles will be recognized. For example, a security
camera might be setup to control access to a parking garage. In
this scenario, it may be imperative that only authorized vehicles
are permitted to enter and furthermore, that those vehicles are
always allowed to pass. To deal with this requirement an
algorithmic subset of Full Recognition Mode LPR known as
"Pattern-matching LPR" is commonly employed.
"Pattern-matching LPR" doesn't rely on complete recognition of the
alphanumeric plate-string to identify a vehicle. Instead,
Pattern-matching LPR stops short of estimating the plate-model and
simply compares (matches) the two vector-models (patterns) that are
derived from two captured raster images. If the mathematical
correlation between the two vector-models is sufficiently high then
the algorithm concludes that the two images deficit the same
vehicle.
In the access control example given above, the vector model's of
all authorized vehicles would be captured a priori and stored in
the system's database, thereby permitting the Pattern-Matching
algorithm to refer to the known vector-model of all authorized
vehicles that request entry to the garage. The vector-models of all
unauthorized vehicles will not correlate to any of the authorized
vector-models and can therefore be denied access to the garage.
Conceptually, Pattern-matching LPR is the same as performing Full
Recognition Mode LPR on the two raster images and then correlating
the two computed plate-strings to see if they contain the same text
(pattern). However, Pattern-Matching LPR has one important
advantage over Full Recognition Mode LPR; since a plate model
contains more mathematically defined spatial information about the
vehicle than a fully recognized plate-string, the correlation
computed between two vector-models is less vulnerable to
vectorization errors than the correlation of two fully recognized
plate-strings. Pattern-matching LPR is therefore more reliable at
determining if two raster images portray the same vehicle. However,
Pattern-matching LPR cannot extract useful information from a
single image and cannot make the legal link to the vehicle's owner
(only Full Recognition Mode LPR can provide that information).
SUMMARY OF THE INVENTION
The present invention overcomes the problems associated with
enforcing parking regulations by automating the manual processes
performed by a Parking Enforcement Officer using the
"tire-chalking" enforcement methodology described above.
Instead of manually applying chalk-marks to each vehicle, a digital
camera captures a raster image of each vehicle along the patrol
route thereby identifying it as being present in its observed
location. A License Plate Recognition algorithm immediately
extracts a unique digital identifier for the vehicle depicted in
each raster image and stores it in a computerized database.
Typically, the unique vehicle identifier is the alphanumeric text
appearing on the vehicle's license plate (the "plate-string"). Each
of the observed unique vehicle identifiers is stored in a database
record that also contains the time it was observed and its
geographic location. The time-stamp of each vehicle ID is typically
read from the computer's internal clock while its geographic
location is typically read from an external positioning system such
as the "Global Positioning System") (GPS).
The driver of the patrol vehicle traces and re-traces the patrol
route in a manner similar to an officer first applying chalk-marks
to all parked cars along the route and then later searching along
the route for marked cars that are over-parked. Each time a new
image of a parked car is captured and its unique identifier has
been determined, the computer searches its database to see if that
vehicle identifier has already been observed by the system. If a
matching vehicle identifier is found in the database, the computer
compares the time and location of vehicle's first observation to
the time and location of its second observation. If this comparison
reveals that the vehicle has been parked at the same location for
longer than the local parking time limit, then the computer sounds
a "parking violation alarm" that commands the driver to stop the
patrol vehicle. The system then prints out a legal parking citation
describing the evidence of the infraction. The officer then
visually verifies the evidence, signs the citation, affixes the
citation to the offending vehicle and continues along the patrol
route.
In an alternate embodiment, the same measurement technique is
employed to determine the period of time each vehicle has been
parked. However, instead of simply testing to see if that period is
longer than the local parking regulation permits, the system uses
the time parked to determine a parking fee and charge that fee to
the vehicle's owner.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of a computer system and sensors,
according to a preferred embodiment of the present invention.
FIG. 2 is a top view of two patrol cars enforcing parking
regulations along public a street, according to a preferred
embodiment of the present invention.
FIG. 3 is a side view of a patrol car enforcing parking regulations
on public streets, according to a preferred embodiment of the
present invention.
FIG. 4 is a top view of a patrol car enforcing parking regulations
in a private parking facility, according to a preferred embodiment
of the present invention.
FIG. 5 is a flow chart of the data processing algorithm used by the
present invention to detect over-parking infractions on public
streets and issue parking citations by means of Full Recognition
Mode LPR.
FIG. 6 is a flow chart of the data processing algorithm used by the
present invention to detect over-parking infractions on public
streets and issue parking citations by means of Pattern-Matching
LPR.
FIG. 7 is a flow chart of the e data processing algorithm used by
the present invention to detect over-parking infractions on public
streets and issue parking citations by means of radio frequency
transponders.
FIG. 8 is a flow chart of the data processing algorithm used by the
present invention to determine the amount of money to charge each
client vehicle for short-term parking along public streets and
collect said funds.
FIG. 9 is a flow chart of the data processing algorithm used by the
present invention to determine the amount of money to charge each
client vehicle for short-term parking within private off-street
parking facilities and collect said funds.
FIG. 10 is a flow chart of the data processing algorithm used by
the present invention to find vehicles that are wanted by law
enforcement authorities.
DETAILED DESCRIPTION
Reference will now be made in detail to preferred embodiments of
the invention, examples of which are illustrated in the
accompanying drawings. Wherever possible, the same reference
numbers will be used throughout the drawings to refer to the same
or like parts.
FIG. 1 is a block diagram of a computer system 100 in accordance
with a preferred embodiment of the present invention. Computer
system 100 includes a host computer 101. Host computer 101 includes
a CPU 102, a memory 103, a communication bus 120, a display device
130 (e.g., a computer monitor), and input/output devices such as a
keyboard 131 and printer 132. Memory 103 may include device drivers
110 and operational software modules. License Plate Recognition
software 111, vehicle navigation software 112 and database software
113 are described in detail below. It will be understood by persons
of ordinary skill in the art that computer system 100 may also
include numerous elements not shown in the figure for the sake of
clarity, such as disk drives, additional display devices, network
connections, additional memory, additional I/O elements, additional
CPUs etc.
Computer system 101 also includes an operating system (not shown),
such as the Windows 95 operating system. "Windows 95" is a
registered trademark of Microsoft Corp. It will be understood that
the present invention is not limited to any particular hardware,
operating system, or type of computer system.
The computer system 101 is mounted within a parking regulation
enforcement patrol vehicle 50 and communicates with external
sensors and sub-systems over the communication bus 120. Various
external sensors and sub-systems are connected to the communication
bus 120 to observed data used by the present invention to enforce
parking regulations. The principal peripherals connected to the
computer 101 are:
A digital video camera 51 mounted on the moving patrol vehicle 50
such that it can capture a raster image of each parked car's
license plate 61 as it transits the camera's field of view. The
image data is communicated to the host computer 101 for processing
by the License Plate Recognition software module 111.
A proximity sensor 52 affixed to the patrol vehicle 50 such that it
senses the presence of passing vehicles and triggers the capture of
images by the digital camera 51.
A positioning sub-system 53 that continually determines the
position of the measurement point 53 on the moving patrol vehicle
50. The measurement point 53 is typically coincident with the
antenna of the positioning sub-system 53. The observed positions
are communicated to the host computer for processing by the
database software module 113 and the navigation software module
112. The positioning sub-system 53 selected for use in the present
invention can be LORAN, GLONASS, GPS, differential GPS,
GPS/inertial, GPS/differential-odometer,
GPS/differential-odometer/fluxgate-compass, GPS/map-matching
etc.
A clock 55 that observes the time and communicates it to the host
computer 101 for processing by the database software module 113.
Typically the clock will be located within the host computer
101.
A printer 132 capable of printing a parking citation is connected
to the host computer 101 a nd used to print parking citations as
required by the database software module 113.
In a preferred embodiment described below, a radio frequency
vehicle identification transponder 54 observes the unique identity
of each parked vehicle and communicates each unique identifier to
the host computer 101 for use by the database software module
113
In preferred embodiments described below, a data link 56 enables
the transfer of information to and from various external databases
maintained by various judicial and/or financial institutions. The
link to data may a realtime communications link to data stored at a
remote site, a real-time link over the local communication bus 120
to data stored on-board the patrol vehicle 50 or a post-mission
link to data stored at a remote site.
Data Collection
FIG. 2, illustrates two instances of the parking patrol vehicle 50
being driven past a line of parked cars 60. A raster image showing
each vehicle's license plate 61 is captured by the video camera 51
into the system's on-board computer 101. Each of these raster
images shows a parked car 60 such that its license plate 61 is
clearly visible.
In a preferred embodiment of the invention, the capture of each
license plate image is initiated manually by an operator who aims
and triggers the video camera when the license plate transits the
center of the camera's viewfinder.
In another preferred embodiment, the capture of license plate
imagery is triggered automatically without the need for manual
aiming by an operator. To provide this function, an electronic
"proximity sensor" 52 is mounted near the front end of the patrol
vehicle 50 and aimed towards the parked vehicles 60.
In a preferred embodiment, the proximity sensor measures the
changing distance between the patrol vehicle and the sides of
parked cars 60. The range finder employed to sense the presence of
each parked vehicle can be one of many "off the shelf" devices
based on optical reflectance, ultrasonic ranging, laser ranging or
other inexpensive distance measurement technology.
To optimize its view angle onto each license plate 61, the video
camera 51 is typically mounted near the rear end of the patrol
vehicle 50. The camera 51 is affixed to the patrol vehicle 50 by
means of an adjustable mounting fixture 57 that permits the camera
to be affixed at any location and orientation relative to the
patrol vehicle 50. The camera is aimed obliquely towards the
approaching parked cars 60, such that the camera's viewfinder
frames the rear end of the parked car as it transits the proximity
sensor's measurement beam 52.
While the proximity sensor 52 is aimed towards the unoccupied space
between parked cards (i.e. while observing a "long" range), it
asserts a "wait" command in the circuitry controlling the
frame-grabber of the digital camera 51 ("frame-grabber" is the term
commonly used for a video camera's "digital shutter"). As the
patrol vehicle starts to transit the rear end of the next parked
car along its route, the proximity sensor observes a rapid
reduction in distance (assuming the proximity sensor being used is
a distance-measuring device). The sudden reduction in distance
output by the proximity sensor is detected by the device driver
software 110 and transformed into a "shoot" command which fires the
digital camera's frame grabber, thereby capturing a clear, oblique
image of the rear end of the vehicle centered on the vehicle's
license plate 61.
In another preferred embodiment, the proximity of each new vehicle
60 along the patrol is detected using an "off the shelf" metal
detection means rather than the "off the shelf" distance measuring
means described above. The camera's frame-grabber is triggered by
the rapid rise in the signal from the metal detector that occurs as
the metal detector comes abreast of the rear end of each parked car
60. To achieve adequate sensitivity, the metal detector's induction
coil may be affixed to the patrol vehicle at the outboard end of a
transversally mounted boom 58, thereby placing the vehicle sensor
in closer proximity to each of the passing parked vehicles 60.
Once the camera 51 has captured an image, it reverts to the "wait"
state, which continues until a sharp decrease in distance is
observed by the range finder 52 (caused by refection form the rear
end of the next parked vehicle). As the patrol vehicle's front end
moves along the side of a parked car and starts to transit its
front end, the proximity sensor 52 observes a rapid increase in
distance that signals the start of the gap between parked vehicles.
The proximity sensor 52 thereby enables the device driver software
110 to estimate the width of the gap in between each pair of parked
cars. The time between the start and end of the gap is therefore
observed and the multiplying it by the velocity of the patrol
vehicle computes the distance across the gap. The distance across
the gap between cars can also be observed the positions output from
the positioning sub-system 53 when the proximity sensor 52 is
triggered by rapid increases and decreases of signal strength.
Sometimes, the rear vehicle 60 is too close to the car in front of
it to permit a clear view of the front vehicle's plate 61.
Therefore, in a preferred embodiment, the device driver 110
prolongs the "wait" state until the camera's line of sight has
transited the front end of the rear vehicle. The required delay
period is computed using the speed of the patrol vehicle, the
observed gap between parked cars and the baseline distance between
the range finder and camera. In FIG. 2, the distance D illustrates
the distance the patrol vehicle should travel before finally
triggering the camera 51. The time delay required to achieve this
optimal geometry is computed by dividing the patrol vehicle's
current velocity by the distance required to achieve a clear line
of sight onto the target license plate 61. The velocity is a
parameter that is typically available from the positioning
sub-system 53.
In another preferred embodiment (not illustrated), a plurality of
distance sensors 52 are mounted along the side of the patrol
vehicle 50, thereby measuring the distance between the side of the
patrol vehicle and the sides of parked cars at multiple locations.
As the patrol vehicle advances along the side of the parked vehicle
60, its front distance sensor eventually starts to measure a long
distance (into the gap between parked cars). The remaining sensors
will still be measuring short distances to the side of the parked
vehicle. As the patrol vehicle advances, the remaining sensors each
start to measure long distances as each comes abreast of the front
of the parked car. Eventually, the front sensor encounters the rear
end of the parked car and is hence directly abreast of the license
plate 61 that must be captured by the camera 51. The devices driver
software 110 keeps track of the changing distances arriving from
each of the distance sensors 51, thereby modeling the gap between
the parked vehicles 60. Knowing the fixed geometry of all the
sensor components affixed to the patrol vehicle as well as the
relative position of the gap between the two parked cars, the
device driver 110 waits until the camera's line of sight has
reached the first location at which the front parked car's license
plate is unobstructed by the rear parked car before triggering the
capture of the front car's digital image. The embodiment
effectively models the distance D shown in FIG. 2.
In a preferred embodiment, the baseline distance between the
proximity sensor 52 mounted near the front of the patrol vehicle 50
and the video camera 51 mounted near the rear of patrol vehicle is
maximized thereby insuring that the camera's line of sight is not
aimed onto the target license plate 61 at too oblique an angle. To
provide an adequately long baseline, the range finder sensor and/or
the video camera may be affixed to the patrol vehicle 50 by means
of a mounting fixture 57 and 58 that may extend somewhat in front
of or behind the patrol vehicle.
The higher the video camera 51 is mounted on the patrol vehicle 50,
the easier it is to see over the hood of the vehicle 60 parked
behind the target license plate 61. Therefore, in a preferred
embodiment, the video camera mount 57 permits the camera 51 to be
affixed high enough above the patrol vehicle 50 to see the target
license plate 61 over the hood of parked cars 60. FIG. 3 is a side
view that illustrates how the camera mount 57 might be deployed in
an operational scenario.
In another preferred embodiment, when the camera's frame grabber is
triggered, it fires rapidly in succession, thereby capturing a
series of images of the same license plate 61. The plurality of
observed images of each license plate insures that the LPR
algorithm used to recognize the vehicle's unique digital identifier
can analyze at least one image that adequately depicts the license
plate 61. While somewhat wasteful of computing resources, capturing
continuous video imagery of the passing parked cars 60 therefore
improves the reliability with which vehicles can be identified.
In another preferred embodiment, all the video imagery observed by
the camera 51 is geo-referenced, time-stamped and archived for
possible use by law enforcement authorities. For example, if a bank
robbery has occurred, police can then review the video imagery
captured by parking patrol vehicles that happened to be working in
the vicinity of the crime to search for visual evidence relevant to
their investigation.
FIG. 4 illustrates cars that are packed side by side rather than
end to end. This parking configuration is typical of how cars are
parked in off-street parking lots. Some municipalities have very
wide road allowances that also permit side by side parking. When
parked side by side, the cars present their license plates 61 in a
more favorable orientation relative to the passing patrol vehicle
(approximately at right angles). Side by side parking therefore
dictates that the camera triggering mechanism on the patrol vehicle
50 must be altered to compensate for the different camera geometry.
Therefore, in a preferred embodiment, the camera 51 can be rotated
from its oblique orientation, (angle forward to capture the license
plates of cars parked end to end) to the substantially orthogonal
orientation depicted in FIG. 4 (angled straight out to capture the
plates of cars picked side by side). Furthermore, the vehicle
proximity sensor used to detect cars parked side by side is mounted
closer to the camera's location (approximately half the width of a
typical car), thereby permitting the proximity sensor to trigger
each image capture when the camera is correctly centered on the
license plate 61.
Another physical characteristic of cars parked in areas that permit
side-by-side parking is exploited by the present invention, thereby
improving the efficiency with which Epoch-IDs are observed. The
roadway separating rows of cars parked side-by-side is often free
of the on-coming traffic, which (on public streets) constrains the
patrol vehicle to observe cars parked along only one side of the
street. To observe all of the cars parked along both sides of the
roadway therefore requires two circuits of the patrol route (one in
each direction). If however the patrol vehicle has unobstructed use
of the roadway (such as in private parking facilities or lightly
traveled public streets) then greater efficiency is obtained by
observing the parked cars along both sides of the roadway at once
(effectively doubling the productivity of each patrol vehicle).
Therefore, in a preferred embodiment (not illustrated), the patrol
vehicle 50 is equipped with two video cameras 51, each camera being
oriented towards one of the two sides of the roadway. Additional
proximity sensors 52 are also provided to trigger each of the two
cameras as each new parked car transits their viewfinder. Since the
proximity sensors will be operating at a greater distance from the
parked cars, less precise triggering of each camera's frame-grabber
will often result. Therefore, the LPR software 111 used to analyze
imagery from both cameras may be modified to place greater reliance
on the analysis of multiple images depicting each parked vehicle
60.
At the same instant that each vehicle's raster image is captured by
the video camera 51, the computer also captures the current time
(from the time sensor 55) and the vehicle's current geographical
position (from the positioning subsystem 53). These three observed
data (time, location and vehicle identifier) are concatenated and
stored as a data record in the system's database 113. Each of these
data records is hereafter referred to as the parked vehicle's
"Epoch-ID".
The geographic coordinates observed by the positioning sub-system
53 define the position of the patrol vehicle 50 rather than that of
the (nearby) parked cars under surveillance 60. The infraction
detection algorithm described below assumes that the coordinates
logged in the Epoch-ID describe the position of each parked car.
Therefore, in a preferred embodiment, the distance observed from
the patrol vehicle 50 to each parked vehicle 60 (measured by the
range finder) is used to estimate the desired geographic
coordinates of each parked car's license plate. The known geometry
between the location of the camera 51, the range finder 52 and the
positioning system's antenna 53 are also used to project a position
estimate for each parked car's license plate 61. The estimated
position of the license plate is then stored in each parked
vehicle's Epoch-ID.
Data Processing
Recognition of Each Vehicle's Unique Identity
As soon as a parked car's geo-referenced and time-stamped raster
image of has been captured into the host computer 101, the computer
applies an "License Plate Recognition" algorithm 111 to the
image.
In preferred embodiments of the present invention, Pattern-matching
LPR and Full Recognition Mode LPR are applied to the captured
imagery (both methodologies are summarized above in the "technical
background"). In preferred embodiments (described below), the two
LPR methodologies are applied both separately and in concert,
thereby optimizing t he system's flexibility and reliability.
The LPR sub-system 111 selected as a component in the present
invention must be fast enough that the time required to uniquely
identify the vehicle in each raster image is somewhat less than the
time it takes for the patrol vehicle 50 to travel the distance
between parked cars 60. Ideally, this processing takes place
quickly enough that the patrol vehicle 50 can be driven at the
normal speed of traffic.
At the same instant that each vehicle's raster image is captured
and a unique vehicle identifier is extracted from it. The system's
computer also captures the current time and the current
geographical coordinates observed by the positioning sub-system.
These three observed data (time, location and unique vehicle
identifier) are concatenated and stored as a data record in the
system's database. Each of these data records is hereafter referred
to as the parked vehicle's "Epoch-ID".
Storage and Use of Data Reliability Indicators
in a preferred embodiment, statistical reliability indicators for
each of the three observed data in the Epoch-ID are computed and
stored along with their respective data element (i.e. the
reliability of each time observation, position observation and
vehicle identity estimate are recorded). These reliability
indicators are subsequently used to enhance the performance of the
real-time violation detection algorithm 101. The reliability
indicators also facilitate several control functions exercised by
the system's operator. The reliability indicators may ultimately be
used as evidence in court to defeat a legal challenge of the
parking citation's validity. The Epoch-ID's three reliability
indicators are described as follows:
1) The Epoch-ID's Temporal Reliability Indicator:
Since infractions are detected by subtracting two time-stamp
observations, the absolute accuracy of each Epoch-ID's time-stamp
is not critical (gross clock errors will be subtracted out by the
detection algorithm). Therefore, as long as the difference between
the two Epoch-ID's time-stamps can be shown to be accurate to
within a few seconds, the temporal component of the parking
infraction detection algorithm will remain sufficiently accurate.
The time sensor 55 used to time-stamp each Epoch-ID will typically
be provided by the standard clock integral to a computer 101.
Provided that the Enforcement Officer periodically verifies that
the computer's clock is not drifting widely from an independent
time source (such as the time announced on a car-radio), the
temporal accuracy of the system can be logged as being better than
a few seconds.
In a preferred embodiment, the system's position sensor 53 is a GPS
receiver that can provide a very accurate time synch output. The
system's local clock uses the time signal to automatically
synchronized to within a few milliseconds of GPS time, thereby far
exceeding the minimum requirement for temporal accuracy.
2) The Epoch-ID's LPR Reliability Indicator:
Many aspects of LPR are stochastic processes. An estimate of the
statistical reliability of each recognized plate-string is
therefore a by-product of most Full Recognition Mode LPR
algorithms. If a Pattern-matching LPR algorithm is applied to the
imagery, the computed correlation factor between the two matched
vector-models is also a good statistical reliability indicator.
Therefore, in a preferred embodiment, a statistical reliability
estimated for the plate-string and/or the pattern-match is included
in each stored Epoch-ID.
To reduce the chance of false alarms from the parking violation
alarm, the Officer can link its sensitivity of the to the computed
reliability of the LPR process. In a preferred embodiment, the
violation alarm's sensitivity is adjusted by varying a threshold
value used to reject LPR data that is not considered sufficiently
reliable. For example, the database software 113 could ignore all
plate-strings whose probability of being true is less than 90
percent. Similarly, if the correlation factor between two
vector-models were less than 0.90, then a match between Epoch-IDs
would not be declared. Raising the value to 95 might reduce the
number of false alarms at the expense of missing some real
opportunities to issue citations. Lowering it to 85 might trigger
the alarm more often however the Officer might also waste more time
visually verifying license plate numbers that turn out to be
incorrectly recognized or matched.
The plate-string recognized in each captured image is the most
succinct digital encapsulation of the vehicle's unique identity
that can be derived from the LPR process. The plate-string is
therefore the best search criteria for quickly searching the
database to detect parking violations. Furthermore, the
plate-string (the license plate number) can also be used to link
into useful external database 56.
While the plate-string has the advantage of being concise, it's the
entire raster image from which the plate-string is derived that is
each vehicle's most complete and unique identifier. Therefore, in a
preferred embodiment, the vehicle's raster image is also stored as
part of the Epoch-ID and archived to enhance the system's
reliability. These images may eventually serve as evidence in
court.
The stored raster imagery also serves as a means for independently
verifying the validity of the LPR process. In a preferred
embodiment, whenever the violation alarm is triggered, the system
immediately displays the two raster images contained in the
Epoch-IDs found to match by the database software 113. Displaying
the images on the system's display device 130 provides an
independent and reliable means of verifying the accuracy of both of
the plate-strings that were estimated by the LPR algorithm. To
verify that each plate-string is correct, the Officer inspects the
displayed images and reads both of their visible license plate
numbers. The Officer then compares the visually read plate-strings
to the matched plate-string that triggered the infraction alarm
(also displayed on the monitor 130). This visual verification step
is carried out by the Officer prior to signing the parking citation
and affixing it to the offending vehicle.
Both the vector-model and the plate-model computed by the LPR
algorithm are essentially less verbose, encapsulations of the same
unique vehicle identifier captured in the raster image. Therefore,
in a preferred embodiment, each raster image's vector-model and/or
plate-model is also stored in the Epoch-ID. By retaining the
vector-model and/or plate-model, they become available for
re-analysis to either: confirm the plate-string, correct the
plate-string or provide an alternate method of matching Epoch-IDs.
The analytical exploitation of interim LPR data is performed by one
of three possible embodiments: 1) In a preferred embodiment, the
Enforcement Officer exploits the vector and/or plate models by
plotting them onto the system's computer display screen. The
Officer inspects the image of the license plate to confirm the
violation prior to signing the parking citation and serving it on
the offending vehicle, (similar to the procedure described above
for verifying the plate-string by inspection of the raster images).
Since vector-models occupy less storage space than raster images
this embodiment eases the load on the system's computing resources.
2) In a preferred embodiment, near real-time re-analysis of the
captured vector models is carried out by a second LPR algorithm
prior to issuing a violation alarm. This embodiment would also be
used as a means to reduce the load on the system's computing
resources. For example, a first Full Recognition Mode LPR algorithm
that is very fast but somewhat less reliable would be used to
initially detect suspected violations. Once a suspected violation
is detected by the first LPR algorithm, a second, more reliable but
slower executing Full Recognition Mode LPR algorithm, would be
applied to the same data, thereby improving the reliability of the
citation. Only after the two Epoch-IDs have passed the second, more
rigorous, extraction of matching plate-strings would the violation
alarm be triggered to alert the system's operator to stop the
patrol vehicle and issue a citation. 3) In a preferred embodiment,
the two vector-models stored in suspected Epoch-IDs flagged by Full
Recognition Mode LPR are then submitted to a Pattern-matching LPR
algorithm. Since the results of a pattern-match are inherently more
reliable than the results of a plate-string, confirmation by this
re-analysis of the data adds to the reliability of citations issued
by the system.
3) The Epoch-ID's Spatial Reliability Indicator
The statistical reliability of the geographical positions used in
the violation detection algorithm are also quality indicators that
add weight to the body evidence that may eventually be presented in
court. Therefore, in a preferred embodiment, an estimate of each
observed position's probable error is made and included in the
Epoch-ID.
The present invention can use an "off the shelf" positioning system
to provide geo-referencing information for each Epoch-ID. Satellite
systems such as the Global Positioning System (GPS) are the
preferred sub-system. Other sub-systems such as LORAN, GLONASS,
differential GPS, GPS/inertial, GPS/different-odometer,
GPS/different-odometer/fluxgate-compass and GPS/map-matching are
also acceptable sub-systems. The integration of GPS with other
sensors is a common practice that is useful in areas of poor
satellite visibility (such as near high buildings). All of the
above positioning system configurations are well-established
technologies that, due to redundant range observations, lend
themselves to real-time error estimation. In a preferred
embodiment, the positional error estimate provided by the
geo-referencing sub-system is stored in the Epoch-ID to provide
Quality Control of the spatial parameter.
The geographic coordinates observed by the positioning sub-system
define the position of the patrol vehicle rather than that of the
(nearby) parked cars under surveillance. The parking violation
detection algorithm described below assumes that the coordinates
logged in the Epoch-ID describes the position of each parked car.
Therefore, in a preferred embodiment, the distance observed from
the patrol vehicle to each parked vehicle (measured by the range
finder) and the known geometry between the location of the camera,
the range finder and the positioning system's antenna, is used to
compute the geographic coordinates of each parked car's license
plate. The estimated position of each license plate (and its
estimated uncertainty) is then stored in the parked vehicle's
Epoch-ID.
Embodiments That Use Full Recognition Mode LPR
FIG. 5 is a flow chart illustrating the procedures implemented by
the present invention to enforce parking regulations by means of
applying Full Recognition Mode LPR. In a preferred embodiment, the
patrol vehicle is driven past a row of parked cars 500 and the
plate-string visible in each successively captured raster image 501
is extracted using Full Recognition Mode LPR 503. Each plate-string
503 is geo-referenced 504 and time-stamped 502. The observed
plate-string, its time of observation and its location of
observation are concatenated into an Epoch-ID record 505 that is
suitably formatted for storage in the system's database 113. The
reliability indicators described above are also concatenated into
the Epoch-ID (not illustrated). To assure the privacy of citizens
(described below) the data is typically encrypted 506 before being
stored in the database 507.
As soon as each Epoch-ID has been entered into the database, the
computer searches to see if the same plate-string has been observed
previously by the system 508. If no matching vehicle identifier is
found 509, then the algorithm reverts to waiting for the next
parked car to be encountered 500.
If a previous instance of the plate-string is found in the database
of Epoch-IDs 509, then the vehicle's previous position coordinates
are compared to its current position coordinates 510.
If the difference between the two observed positions reveals that
the vehicle has not moved an appreciable distance since its
previous observation, then the system flags the vehicle as being
under suspicion of violating the parking regulations 510. If the
distance between the two observations is substantially different,
then the algorithm goes back to waiting for the next parked car to
be observed.
In a preferred embodiment, the positional test for whether or not
the observed vehicle has moved "substantially" includes a distance
tolerance to compensate for the instrumental error inherent to the
positioning sub-system 53 (QC information already recorded during
step 505). For example, if the positions submitted to the
immobility test 510 have an estimated accuracy of +/-3 metres, and
the distance between the coordinates is 2 meters, then a tolerance
of 3 meters will insure that the vehicle is flagged as having been
immobile (even though the two positions are not numerically
identical).
The test criteria for determining if the vehicle has moved an
"appreciable" distance may also include a required radius of
movement stipulated in municipal parking regulations. For example:
a municipality's parking regulations might stipulate that a car
must be moved at least 200 m in order to "restart" its legal
parking status. Including this 200-meter radius in the positional
tolerance insures the motorists who have not moved their vehicle
the required distance will be flagged for a parking citation.
When a suspect vehicle has been flagged 510, the system then
subtracts the time stamps of the two Epoch-IDs under suspicion 511,
thereby determining the period of time that the car has been parked
at that location.
The system then searches a database of geo-referenced parking
regulations (described below) to determine the legal time limit
that's applicable to the parked vehicle's present location 512.
If the elapsed time is greater than the time period legally
permitted for that location, then the system declares a parking
violation 513. If the time the vehicle has been parked does not
exceed the time permitted then the algorithm goes back to waiting
for the next parked car to be observed 500.
When the system declares a parking violation, a "parking violation
alarm" is triggered 514, thereby alerting the Officer to stop the
patrol vehicle. The "parking violation alarm" used to alert the
Officer may be a visible message displayed on the CRT 130.
Alternatively, the parking violation alarms can be an audible sound
from the speaker normally integral to the computer 101.
In a preferred embodiment, when the parking violation alarm is
triggered, the computer 101 displays the two stored images of the
parked vehicle on the display monitor 130. The Officer then
verifies that the two images appear to portray the same vehicle.
The Officer also visually verifies that the two plate-strings are
identical to the plate-string recognized by LPR analysis 515.
After the LPR vehicle identification has been independently
verified, the system uses the output device 131 to print an
official parking citation that summarizes the evidence relevant to
the parking infraction 516. The printed parking citation will
typically display (at least) the following data elements: 1) The
two matching plate-strings (license plate numbers) that were
recognized from the two digital images of the (same) parked car 60.
The original raster or vector imagery may optionally be printed on
the citation. 2) The time that each of the two license plate
numbers was observed. The elapsed time as well as the permitted
parking period may optionally be printed onto the citation. 3) The
geographical coordinates of the two independent sightings of the
parked vehicle 60. The distance between the two observations may
optionally be computed and shown on the citation. 4) The in
formation needed to describe the local parking regulation that has
been contravened (the parking time limit at the time of the
infraction, the street name where the infraction occurred,
etc.).
As soon as the parking citation has completed printing (typically
the time it takes to stop the patrol car 50 and back up to the
offending parked vehicle 60) the Enforcement Officer signs the
citation, serves it on the vehicle's owner by attaching it to the
windshield of the offending vehicle and then continues driving
along the patrol route towards the next parked car 500.
Embodiments That Use Pattern-Matching LPR
FIG. 6 is a flow chart illustrating procedures implement ed by the
present invention to enforce parking regulations by means of
applying Pattern-Matching LPR to the captured images. This
algorithm is very similar to that illustrated in FIG. 5 however
Full Recognition Mode LPR image analysis 503 is replaced with
Pattern-Matching image analysis that simply derives a vector-model
from the license plate image 518. The patrol vehicle traces and
re-traces the patrol route in the manner described above. However,
instead of searching the database for a matching plate-string, the
algorithm searches the database for a matching plate-model 519. The
added certainty with which the algorithm can match the more
detailed vector-models improves the certainty with which the system
can declare a vehicle to have been re-observed 520. Using
pattern-matching LPR therefore improve s the accuracy and
reliability of the parking violation alarm 514.
When a pattern match is found, the system tests to see if the two
matching Epoch-IDs were observed at substantially the same location
510 and if the time between the two observations exceeds the
permitted parking limit 513. If the position and time tests reveal
that a parking infraction has occurred, then the violation alarm is
triggered 514.
The Officer then stops the patrol vehicle however the offending
parked vehicle's license plate number is still unknown and must
therefore be identified manually. To facilitate identifying the
vehicle, the system displays the two raster images and/or
vector-models that triggered the alarm. The Officer then visually
inspects the images to verify that the vehicle captured at the
first and second observation epochs appears to be the same make and
model of car. If the violation alarm passes the visual inspection,
the Officer then reads and enters the alphanumeric characters on
the vehicle's license plate into the system where it becomes the
plate-string inserted into each of the Epoch-Ids 521. Data entry is
typically effected using the keyboard 131. The human-read
plate-string is flagged as such in the Epoch-ID, thereby certifying
its high level of certainty in the event that the parking citation
is contested in court. Once the plate-string has been entered, the
system proceeds to print out the parking citation 516, the Officer
serves it 517 and proceeds along the patrol route 500.
In another preferred embodiment (not flow-charted), the Pattern
Matching LPR method is first used to detect a suspected parking
infraction. Once the violation alarm has been sounded, the system
applies then the less reliable (but more useful) Full Recognition
Mode LPR algorithm is applied to the two images of the vehicle's
license plate. If the plate-strings extracted from the images are
identical then the imagery and alphanumeric data are displayed to
the Officer for visual inspection and certification. If the
plate-string does not match the Officer's visual interpretation of
the imagery (for example, the numeral "zero" may have been
recognized as the letter "O" by the LPR algorithm) then the Officer
edits the plate-string. Since the plate-string will, in most cases,
be correct the Officer's data input workload is thereby
reduced.
The database search to Pattern-Match plate-models 519 is more time
consuming than when Full Recognition Mode LPR is used to find
matching plate-strings 509. This is because searching for a
specific alphanumeric plate-string is faster than searching for a
relatively complex vector-model, particularly as the number of
Epoch-IDs in the database grows large. To address this speed
limitation, a preferred embodiment of the present invention
utilizes the information available from the positioning sub-system
53 to accelerate the search for matching vehicle identifiers. As
each new Epoch-ID is observed 505, instead of searching through
thousands of records in the database for a matching vehicle
identifier, the database first searches to find those few Epoch-IDs
that are positioned a short distance from the current Epoch-ID.
Conducting a preliminary "nearest neighbor" search of the database
is much faster than trying to find a matching plate-model 520. By
localizing the search to only those few vehicles that might
conceivably be the same (over-parked) vehicle, extra processing
time becomes available for carrying out more sophisticated LPR
analysis.
The distance criterions used to search for "nearest neighbor"
Epoch-IDs will typically be the same distance criterion used by the
violation detection algorithm to test if a vehicle has moved
"appreciably" between observations (described above). The nearest
neighbor distance criterion may therefore be comprised of a
distance to compensate for instrumental noise in the positioning
system as well as a regulated distance imposed to force motorists
to completely vacate the vicinity after legally occupying a parking
spot.
Therefore, in a preferred embodiment, the database search 519 also
comprises a preliminary nearest neighbor search to find Epoch-IDs
located a short distance away from the patrol vehicle's current
geographic location. The few Epoch-IDs found by this preliminary
search are then searched to see if there are any matching
plate-models. If a plate-match is found, then the rest of the data
processing proceeds as illustrated in FIG. 6.
In another preferred embodiment (not illustrated), the Full
Recognition Mode LPR methodology is first applied to all captured
imagery. If however the estimated reliability of either of the
plate-strings matched in step 509 does not meet a user defined
confidence tolerance, then the algorithm will perform a second
iteration of analysis on the two candidate images in an attempt to
match their vector or plate-models. Since there is more information
contained in two vector-models (fenders, windows, tires, visual
artifacts on the license plate etc.), their mathematical
correlation can be determined with greater confidence than when
correlating two fully recognized plate-strings. Only after the two
candidate images have passed this second iteration of LPR analysis
does the algorithm proceed to test for a parking violation.
Embodiment That Uses Radio Frequency Transponders For Vehicle
Information
The embodiments described above rely on LPR technology to uniquely
identify each of the parked cars along the patrol route. LPR has
the significant advantage of being an entirely passive means of
determining the unique identity of vehicles (i.e. all registered
vehicles are already equipped to be identified). To facilitate new
highway applications such as automated toll collection, there is a
growing trend to provide an active means for identifying each
vehicle (i.e. means that demand a component be affixed to each
vehicle in order for it to be recognized). Various means have been
developed for actively identifying vehicles. Bar codes imprinted
onto each vehicle have been proposed. The bar codes are
subsequently optically scanned to determine the vehicle's identity
and are therefore functionally equivalent to LPR of the vehicle's
alphanumeric license plate number. Magnetic encoding strips affixed
to each registered vehicle has also been proposed to as a means for
actively identifying vehicles.
Another proposed means for actively identifying each vehicle is
provided by a low-power radio-frequency transponder affixed to each
vehicle monitored by the system. Each mobile transponder responds
to a low-power interrogation signal broadcast from stationary
highway infrastructure (such as an automated tollbooth). The low
broadcast power of the mobile and stationary transponders limits
their range such that each mobile transponder is triggered only
when it is in close proximity to the stationary transponder. When
interrogated, the vehicle's transponder responds by broadcasting a
low-power radio-frequency data signal that contains the vehicle's
unique identification code (e.g. its license plate number). In the
future more and more vehicles will be equipped with active
identification means such as transponder, therefore transponder
technology provides a viable alternative to using LPR technology in
the present invention.
FIG. 7 is a flow chart illustrating procedures implemented by the
present invention to enforce parking regulations using a
radio-frequency transponder 54 affixed to the patrol vehicle 50 as
the means for observing the unique identity of each vehicle
encountered along the patrol route. The unique digital identity
broadcast from the parked car 60 in is received by the patrol
vehicle's transponder 54 and inserted into the Epoch-ID 522 (in
lieu of a unique vehicle identifier derived by LPR). Once the
parked vehicle's transponder ID has been received, geo-referenced,
time-stamped and inserted into the Epoch-ID, the database search is
conducted to search for a matching vehicle ID 524. If a match is
found, all aspects of processing proceed as described above for
detecting parking violations using LPR technology.
In another preferred embodiment (not illustrated), the present
invention employs both LPR and transponder technology to observe
each parked car's unique vehicle identity along the patrol route.
Vehicle's without transponders are identified using LPR as
described above. However, those parked vehicles equipped with a
transponder will respond to the patrol vehicle's interrogation,
thereby providing a redundant vehicle identifier that the system
adds to those vehicles' Epoch-IDs. This redundant vehicle
identifier adds to the body of evidence used to support claims
against the vehicle's owner.
Embodiment That Enables Charging Pay Per Use Fee
The preceding embodiments emulate the tire-chalking methodology
commonly used to detect illegally parked cars and levy to financial
penalty on their owners. This "penalty mode" of parking enforcement
cannot accommodate the collection of modest rental fees from
vehicles that are legally parked for short periods. This
shortcoming dictates that most motorists under surveillance by the
system will in fact park for free (as long as they do not park
longer than the arbitrary time limit). This is a serious financial
drawback when compared to parking meters. Parking meters require
motorists to insert coins, thereby providing a continuous revenue
stream to the municipality, even if no cars are ever convicted of
over-parking.
The tire-chalking methodology lays the entire financial burden of
the system on tho se few motorists caught over-parking and this
contributes to a public perception of unfairness. For example: two
motorists park in the same two-hour zone at the same time. The
first motorist returns one minute before the patrol vehicle makes
its second round and thereby escapes without having paid any
parking fee. The second motorist arrives only two minutes later to
find a $25 parking citation. This perceived unfairness adds to the
psychological stress endured by all motorists.
Therefore, in a preferred embodiment, the present invention
provides means that enable the Parking Authority to collect modest
rental fees from those vehicles that are legally parked for short
periods along public streets (in a manner analogous to that used in
the parking meter enforcement methodology). The net effect of using
this embodiment is to transform all of the real estate along the
municipality's streets into a "pay per use" parking lot without
having to install and maintain physical parking meters.
To provide the necessary revenue collection means, the previously
described embodiments that emulate tire-chalking enforcement are
linked to a central, computerized billing system 56. When the data
observed by the patrol vehicle is linked to an external database 56
at a financial institution, the present invention can emulate the
"pay per use" financial structure inherent to parking meters.
Motorists (hereafter referred to as "clients") wishing to making
use of the "pay per use" parking service must enter into an
agreement giving permission to the Municipal Parking Authority to
withdraw funds from the client's electronic banking facility 56.
Funds withdrawn by the Parking Authority are acknowledged to be
fees for the amount of time each client has spent parking along
city streets. The client's electronic banking means 56 used to
transact the agreement may take the form of the client's credit
card number to which parking fees are to be charged. Various other
"debit card" or "smart card" variant are also adequate electronic
billing and collection means. Alternatively, the client may
authorize the Parking Authority to periodically mail an extract
from its database 56 that details the client's parking activity and
the resulting parking fees that the client owes to the Municipal
Parking Authority.
To improve fee collection performance, the frequency at which the
patrol vehicle 50 observes each parked car 60 along the
surveillance circuit is increased with respect to that of the
"penalty-mode" embodiments described above. For example, if a
particular street has a two-hour maximum parking limit, then the
parking patrol vehicle 50 would normally return to each parking
spot once every two hours (thereby detecting over-parked vehicles
liable for a parking citation). However, if the patrol vehicle
follows a shorter route such that all vehicles are observed more
often (e.g. once every half-hour), then the Epoch-IDs observed for
each vehicle provide the information needed measure the period of
time that each vehicle has been legally parked. This timing
function is the means by which each client's parking bill is
computed. The system measures each client's billable parking time
in multiples of the patrol vehicle's route repetition frequency.
For example, if the system's data capture and processing is fast
enough to permit the patrol vehicle to be driven at normal traffic
speed, then ten city blocks of high-density parked cars might be
observed every 15 minutes. This 15 minute route repetition
frequency results in parked cars being charged for each 15 minute
period that they remain parked at the same location.
FIGS. 8 is a simplified flow chart illustrating the procedures used
to time client vehicles and collect their parking fees. In FIG. 8,
data collection and database loading proceed in the same manner as
that described above (steps 500 to 507). However, the database
search for matching vehicle identities 525 also includes searching
a list of vehicle IDs that are clients registered in the system's
external bill collection database 56 (in addition to searching
through the list of Epoch-IDs that have been previously observed by
the system's sensors).
If the database search 525 reveals that the current vehicle is not
a registered client, then that vehicle cannot be electronically
billed for short-term case for issuing a punitive parking citation.
The algorithm therefore branches 527 to the steps flow-charted in
FIG. 5 (steps 508 to 517).
In a preferred embodiment, observed vehicles that have not been
registered as clients of the system are initially served with a
printed warning to do so or be faced with being served a punitive
parking citation in the future. Repeated warnings may escalate in
their aggressiveness from "polite reminder" to "final notice". The
central database maintained by the Parking Authority 56 keeps track
of these warning notices and communicates the history to the local
database software 113 so that the number of previous warnings can
be viewed on-site by the Officer. If the motorist has ignored too
many warning notices, then the Officer can elect to serve the
vehicle with a punitive parking citation or even impound the
vehicle.
If however the database search 525 reveals that the current
vehicles is a registered client but that it has not been previously
observed by the system's sensors, then a "virtual-parking-meter" is
initialized in the database for that vehicle 528. The term
"previously" is relative and would typically be limited to scope.
Logical limits on the period used to constrain the database search
for a "previous" sighting of a particular vehicle might be: "has
this vehicle been observed within the previous 12 hours, 24 hours
or 48 hours".
Each newly initialize "virtual-parking-meter" is a database record
containing the cumulative evidence that a client vehicle has been
observed parking at a particular geographic location at a
particular time. Each virtual-parking-meter therefore also contains
a data field to contain the elapsed "time-showing" since the
vehicle was first observed to be parking at that location. The
virtual-parking-meter's "time-showing" field is therefore
initialize to zero.
On subsequent circuits of the patrol route, each time the patrol
vehicle observes the same client vehicle and verifies that it has
not moved 529 (i.e. it is still parked at its
virtual-parking-meter), the algorithm subtracts the time of the
vehicle's previous observation from that of it current observation
511 and adds the resulting period of time to the "time-showing"
field of the virtual-parking-meter 530.
If the patrol vehicle's second visit to the location of an existing
virtual-parking-meter reveals that the vehicle's unique identifier
has changed from that observed on the previous circuit, then the
algorithm assumes that the previous occupant was parked for less
than the patrol vehicle's observation period (step not illustrated
in FIG. 8). Since there are no start and stop observations which
evidence the true length of time the previous occupant was parked,
the previous occupant's virtual-parking meter is deleted with no
time showing on it and no parking fee is charged to its owner's
electronic banking means (steps 530, 531 and 532 described below).
Alternatively, since the observed evidence does show that the
vehicle was parked for at least some fraction of the patrol
vehicle's observation period, a minimal "flat-rate" fee is charged
to the client's electronic banking means. In either case the
virtual-parking-meter is deleted from the database and (if a new
tenant vehicle is observed at that location) a new
virtual-parking-meter is initialized.
During subsequent circuits of the patrol route, each time the
patrol vehicle passes in close proximity to the location of an
existing virtual-parking-meter, the identity of its tenant vehicle
is observed. If the same vehicle identifier is observed as on the
previous circuit 529, the elapsed time between it previous and
current sightings is computed 511. The "time showing" field in that
tenant vehicle's virtual-parking-meter is then augmented by the
time interval between its previous and current sightings 530. As
long as that virtual-parking-meter continues to be occupied 531,
new observations of its tenant are made 500 and its "time showing"
continues to be augmented by each circuit's duration.
When the patrol vehicle eventually observes that a
virtual-parking-meter has been vacated by its tenant vehicle 531,
then that virtual-parking-meter's accumulated "time-showing" is
transformed into a monetary value 532 based on the fee structure
defined in the client's agreement with the Parking Authority
(maintained in the Parking Authority's database and available via
the data link 56). The accumulated parking fee is then deducted
from the client's electronic banking means 553 maintained in the
client's Financial Institution's database.
In a preferred embodiment, the parking fee structure charged per
unit time is defined so as to encourage motorists to respect the
Parking Authority's traffic management priorities. For example, a
client parked in a busy commercial district might be charged $0.25
for the first half-hour, $0.50 for the second half-hour, $5.00 for
the third half-hour and $20.00 for parking past the local parking
limit. This type of non-linear fee structure effectively
encompasses the punitive penalty heretofore imposed on vehicles
observed parking for long periods in areas where the Parking
Authority wants to encourage short-term parking.
Various fee structures may be stored in the database 56 and applied
at different times of the day, on different days of the weeks and
in different months of the year. Various fee structures may also be
stored and applied to different vehicles based on the location or
time of day that they were observed parking. For example, clients
who are residents of a particular street may be exempted or charged
a reduced fee for overnight parking on their street.
In a preferred embodiment, if an offending vehicle displays a
handicap sticker that exempts the vehicle from parking regulations,
the system verifies in the database to see if that vehicle is
registered to a legitimate handicapped motorist. If fraud is
suspected, the Officer may choose to wait for the motorist to
return to the vehicle and then take appropriate action.
In a preferred embodiment, if the parked vehicle is registered to a
car rental agency, the parking fee is automatically transferred to
the car rental agency's electronic banking means so that the fee
can be added to their client's rental account.
In a preferred embodiment, out of town or out of state vehicles
detected by the system may be issued with a citation thanking them
for visiting the municipality and wishing them a pleasant
visit.
Linking the sensor data observed by the present invention to an
external electronic billing means provides a number of advantages.
By automating all transactions, the cost of administering the
enforcement system is reduced. Furthermore, direct electronic fund
withdrawal renders the enforcement system more resistant to
scofflaw motorists. Furthermore, since the present invention
reduces the overall cost of parking enforcement, those cost savings
can be applied to reduce the total amount each must client pay for
on-street parking. Furthermore, the time-consuming tasks of feeds
coins into a parking meter (or paying citations for over-parking)
are eliminated, thereby providing a more convenient parking
experience of each client motorist.
Embodiment That Exploits a Fleet of Public Transit Vehicles
The embodiments described above typically maintain the observed
Epoch-IDs in a database that resides on-board each mobile patrol
vehicle (i.e the data link 56 extends no further than the local
communication bus 120 and a local mass storage device in the
computer 101). The system thereby benefits from the high bandwidth
of the communication bus 120 to rapidly search for matching vehicle
identifiers in the database of Epoch-IDs. Maintaining the database
on-board the mobile vehicle is expedient for detecting parking
violations in real-time so that the citations can be printed and
immediately served on each offending vehicle.
However, the data link 56 used to store each Epoch-ID and search
the database of previously observed Epoch-IDs may also be a
high-speed wireless communication link that accesses a remote
database. The data link 56 may also be a hardwired link that
permits all of the data on-board the patrol vehicles to be uploaded
to a central database (typically performed when the patrol vehicles
return to a central garage facility at the end of each
workday).
Therefore, in a preferred embodiment (not illustrated), each patrol
vehicle contributes all of its observed Epoch-IDs to a remote
database (by means of either a real-time or post-mission link 56).
No attempt is made to serve printed citations on over-parked
vehicles therefore no real-time data analysis is required. Data
from all patrol vehicles is simply accumulated into a single
database for a considerable length of time (typically a full
working day). At the end of the working day, all of the data are
then batch processed in the Parking Authority's central database.
All of the financial transactions resulting from the batch
processing are also carried out as a batch process between the
Parking Authority's database and the various database maintained by
Financial Institutions named in the parking agreements previously
established with each parking client.
The advantage of pooling all data observed by the entire fleet of
patrol vehicles is that different patrol vehicle's can be used to
observe each of the sequential Epoch-IDs used to augment the
"time-showing" on each client's "virtual-parking-meter". Since
different patrol vehicles can be used to augment each parked car's
virtual-parked-meter, the frequency with which each parked vehicle
is observed is effectively increased. For example, a patrol vehicle
might traverse a long route that only permits it to observe all
parked vehicles once every 2 hours. However, if eight surveillance
vehicles patrol that same route (while maintaining approximately
equal spacing between them) then each parked car along the route
would be observed approximately once every 15 minutes.
Concatenating all of the Epoch-IDs observed by all eight patrol
vehicles would permit each parked vehicle's virtual-parking-meter
to be re-observed and updated once every 15 minutes.
This operational scenario provides significant cost savings when
the patrol vehicles used to observe parked cars are Public Transit
vehicles such as buses. Each bus is already traversing a
pre-defined route as part of its primary function (transporting
pedestrians along the bus-route). Each bus also passes in close
proximity to all vehicles that are parked along the bus route. If
each bus is equipped with the sensors and computing hardware
described above, and all of the buses' observe Epoch-IDs as they
perform their primary function, then batch processing their data
sets will produce revenue from all of the vehicles parked along the
route. Motorist parking along the bus routes would thereby
effectively subsidize the Public Transit system.
Therefore, in a preferred embodiment, each vehicle in a fleet of
patrol vehicles (such as Public Transit vehicles) observes
Epoch-IDs and stores them in a central database. The accumulated
data is periodically batch-processed such that parking fees are
computed for all virtual-parking-meters observed along each route
and collected in the manner described above.
Embodiments That Collect Revenue in Private Parking Facilities
Heretofore, owners of off-street parking facilities have been
obliged to provide complex and expensive means for collecting
parking fees from their clients. Typically, they control access to
their private property in order to insure that parking fees are
collected. An entry gate is used to prevent vehicles from entering
their facility until the motorist has received a time-stamped
entry-ticket. A second gate prevents vehicles from existing
facility without paying the appropriate parking fee. Typically, a
cashier at the exit gate collects the parking fees. The cashier
receives the motorist's time-stamped entry-ticket, computes the fee
corresponding to the time elapsed since the entry-ticket was issued
and then lifts the exit gate once the parking fee has been paid by
cash transaction. Providing these fee collection means contributes
significantly to the operational cost of private parking
facilities.
FIG. 9 flow-charts a preferred embodiment of the pre sent invention
that significantly reduces the operational cost of private parking
facilities. The procedures used by this embodiment are virtually
the same as those described above for collecting parking revenues
on the public streets and illustrated in FIG. 8. When equipped with
this embodiment of the present invention, the off-street parking
facility does not require any entry or exit controls (i.e. all
vehicles are permitted to come and go freely as parking space
permits). Furthermore, no personnel are required to be on-site to
collect parking fees from clients.
To use this embodiment, an administrative agreement is made between
the owner of the parking facility and the municipal Parking
Authority. The effect of this agreement is illustrated in step 537.
Under the terms of this agreement, parking patrol vehicles owned by
the municipal Parking Authority are permitted to extend their
on-street enforcement routes through the owner's private parking
facility. While the patrol vehicle is operating inside each
off-street parking facility, it observes the time each vehicle
spends parked at its virtual-parking-meter and determines the time
that each client vehicle has been parked at its
virtual-parking-meter (using essentially the same algorithm
described above and illustrated in FIG. 8). The parking fee
determined in step 536 is computed according to the private
facility's posted fee structure. The computed parking fees are then
charged to each client's electronic banking means 533. The Parking
Authority then returns an agreed upon percentage of total revenues
to the parking facility's owner 537. The Parking Authority thereby
optimizes the productivity of its fleet of patrol vehicles while
the cost of operating each off-street parking facility is
reduced.
In order to minimize financial losses caused by non-client vehicles
parking in the facility, the patrol vehicle may impose more
stringent measures against non-client motorists than when
non-clients are encountered on public streets 535. For example, the
facility's owner might prominently post a sign at the entrance
warning all motorists that non-client (i.e. Illegitimate) vehicles
will be towed away or immobilized. The patrol vehicles would
re-observe the number of non-client vehicles in the facility each
time it is patrolled. The financial losses caused by illegitimate
parking clients would therefore be known at all times and could
factored into the revenue sharing agreement with the facility's
owner 537. If losses go beyond an acceptable limit, then
enforcement activities (towing etc) could be escalated as required.
Alternatively, an automated entrance gate could be installed that
is controlled by a stationary video camera and LPR surveillance
system (not illustrated). Only vehicles that are recognized as
clients by the LPR surveillance system would be permitted to
proceed past the automated entry gate into the parking
facility.
To enable the Parking Authority's patrol vehicles to observe
vehicles in off-street facilities, suitable modifications are made
to the sensor system used to trigger data collection 534. The
camera 51 and triggering mechanism 52 mounted on each patrol
vehicle 50 are temporarily modified to compensate for the "side by
side" vehicle orientation normally encountered in off-street
parking facilities (suitable camera mount and triggering means are
described above under' Data Collection"). When a patrol vehicle
enters a private parking facility, its operator must temporarily
re-position and re-orient the vehicle's camera and triggering
mechanisms in order to capture adequate unique vehicle identifiers
while operating within the facility.
Similarly, while the patrol vehicle is operated within an enclosed
parking facility (above or below ground) its positioning sub-system
53 must be suitably modified to provide adequate geo-referencing
capability. For example, since satellite signals will be blocked
while operating within the facility, alternate positions sensors
must be available for establishing and reading the status of each
virtual-parking-meter. Inertial sensors, Dead Reckoning sensors
(such as differential odometers, flux-gate compass, etc) and
map-matching sensors are all suitable technologies for enabling
this embodiment. The geo-referencing information is used to
determine the location of virtual-parking-meters established within
each private parking facility. The Parking Authority can thereby
distinguish revenue collected in each private facility from revenue
collected on public streets and re-direct the correct amount of
money back to the owner each private parking facility.
Embodiment That Provides a Locator Map to Client Motorists
One inconvenience often encountered in large parking facilities is
that motorists cannot remember where they parked their cars. This
problem is particularly acute in large parking lots at major
airports, where motorists often leave their car parked for many
days. After returning from their trip, many motorists have
difficulty remembering where their car is parked. The present
invention provides a solution to this problem by capturing both the
location and identity of all vehicles in the parking facility
during the course of its patrols. This spatial information can be
displayed on a map display (described below) such that a client who
cannot remember where their car is parked can see it plotted on a
"locator-map" of the parking lot.
Therefore, in a preferred embodiment (not illustrated), the same
system used to establish and patrol virtual-parking-meters in a
large parking facility is made available for consultation by
clients who have lost track of their parked vehicles. Typically,
the client motorist goes to a kiosk where the system 101 is visibly
located and enters the license plate number of their lost vehicle
into the database software 113. The system then uses this criterion
to search its database 56, thereby finding Epoch-IDs observed by
the patrol vehicle 50 wherein the unique vehicle identifier
(determined by LPR) matches their search criterion. The location of
their vehicle (contained in the matched Epoch-IDs) is then used to
plot their car's position relative to a digital map of the
"streets" within the parking facility. The resulting composite
image is projected onto the same display device 130 used to provide
navigational guidance to the system's operator during data
collection activities (described below).
The "locator-map" can also be output on the printer 132, thereby
providing the motorist with hardcopy directions of how to find the
lost vehicle. This hardcopy might also display a textual summary of
the times their vehicle was observed at its virtual-parking-meter,
its accumulated "time-showing" and the fee deducted from the
client's electronic banking means (i.e. it also acts as the
client's purchase receipt).
In parking facilities that are already equipped to collect fees by
conventional means (entry/exit gate, time-stamped entry tickets and
cashier-booth), the present invention would not normally be
required as a means of collecting parking fees. However, the
"locator map" function described above might still be considered a
valuable enough customer service to warrant use of the present
invention. In those facilities, collecting fees from
virtual-parking-meters would not be the purpose of the patrols: the
"locator map" would be the sole information product produced by the
system. Since "time-showing" on "virtual-parking-meters" would not
need to be established and continually updated, the frequency of
observation patrols could therefore be reduced considerably. For
example, instead of a half-hour patrol frequency, a six-hour patrol
frequency might be considered adequate for providing locator-maps
to clients. Alternatively, the parking lot management might elect
to perform more frequent patrols simply as a means of increasing
surveillance and improving security for their clients. In the event
of theft or vandalism within the facility, the present invention's
video record of where and when license plates were observed within
the facility would make it useful in deterring crime.
In another preferred embodiment, the "locator-map" function
describe above is provided to clients on a mobile platform (rather
than forcing the clients to make their way to an information kiosk
to query the system). The most efficient means of providing this
service to the clientele is to permit the operator of a patrol
vehicle 50 to interrupt patrol activities at the request of any
client. For example, if a client cannot find their parked car, they
need only wait for a patrol vehicle to pass nearby whereupon they
would hail the driver to stop. The driver would then input the
license plate number of the lost vehicle for the client and produce
the "locator-map" as described above. If the parked car is a
considerable distance away, the driver might elect to transport the
passengers to their car, either as a courtesy or as a paid
service.
Embodiments That Provide Navigational Guidance to the Operator
In a preferred embodiment, the positioning sub-system 53 used to
geo-reference the observed Epoch-IDs also aids the Enforcement
Officer to plan and follow an optimal parking patrol route. The
planning/guidance means is comprised of a digital map display 130
that uses real-time data from the positioning sub-system 53 to plot
the patrol car's changing location onto a digital image of the
local street map.
To plan an optimal patrol route, the Officer uses the map display
and a computer-pointing device to choose a series of waypoint
locations that define a patrol route. The external database used to
control the displayed map imagery contains geo-referenced
information on the location and name of each street however it may
also contain more detailed information such as: direction of one
way streets, local speed limits, location of municipal
infrastructure etc. Knowledge of the location of municipal
infrastructure is used to test if an observed vehicle is parked too
close to a fire hydrant, loading zone, driveway etc. The database
of such information is commonly referred to as a "Geographic
Information System" (GIS). The GIS is accessed by the system's
computer 101 and database software 113 by means of the link to
external database 56. Since the GIS information is required in
real-time, it will typically be linked from a copy maintained
locally on-board the patrol vehicle 50 (since accessing a remote
database that would place excessive demands on the data link)
The map database also contains geo-referenced information
describing the different parking regulations that apply along each
street at different times of the day. Geo-referenced information on
local parking regulations is required by the fundamental
algorithmic test 513 that is used to flag each parking violation
(i.e. "has this car been parked longer than the local parking
regulation permit!") In order to answer that question, it's evident
that the permitted time limit must be available to the algorithm.
The permitted time limit can change abruptly from one location to
the next and from one moment to the next. Therefore the permitted
parking time limit must be referenced within the database 56 such
that the patrol vehicle's position and time can be used to find the
appropriate parking time limit for each parked car being tested in
step 513.
For violation detection purposes, it's important that the Officer
be able to navigate the patrol vehicle 50 along the planned route
while closely adhering to a tight schedule. The reason for this
timing constraint is that the patrol vehicle must return for the
second license plate observation soon after each vehicle has
started to violate the local parking limit. If the patrol vehicle
returns too early, then the violation alarm will not be triggered
(the time difference between the two Epoch-IDs will not trigger the
parking violation alarm). If the patrol vehicle returns too late,
then real parking infractions will go undetected during the delay.
Either way, the system would perform sub-optimally.
Therefore, record keeping and route prediction means are built into
the guidance system software 112, which help the Office plan and
follow an optimally timed patrol route. The system continually
records and updates a history of ticketing performance and average
road speed along each street in the municipality. The system also
tracks how ticketing performance varies at different times of the
day along each street. This database of historical information is
used to compute an estimate for the time it will take to complete
any proposed patrol route the operator might define on the moving
map display 130. The operator uses this function to interactively
plan a route. When in planning mode, the navigation software 112
automatically sums the probable time it will take to follow a
proposed route circuit and signals when a proposed circuit provides
a optimal productivity.
Furthermore, as the chosen route is being followed, the system
monitors progress in real-time to see how well the driver is
following the schedule (i.e. Arriving "just-in-time" for the second
license plate observation). Based on how well the planned route
schedule is being adhered to, the systems provides suggestions to
increase speed, decrease speed or alter the pre-defined route.
The present invention provides a parking regulation enforcement
system that can withstand legal challenges. Its inherent legal
strength is due to two characteristics.
1) The detection of violations is inherently biased towards the
accused motorist's presumption of innocence. Any errors in
reorganizing a vehicle's unique identity will favour non-detection
of a parking violator rather than false accusation. An incorrectly
recognized license plate number cannot be flagged as a violation by
the algorithm because it won't be able to find a matching Epoch-ID
in the database. This bias towards the innocence of violators may
result in a small number of real parking violators being missed by
the system however it also insures a high probability that all
detected parking violators are in fact guilty as charged.
2) Because the reliability of each Epoch-ID's data elements is
statistically qualified, the evidence presented in court to
corroborate the accusation is demonstrably sound. To add further
legal weight to each accusation, the Parking Control Officer can
testify to having visually verified that the license plate number
recognized by the system was identical to that of the accused
vehicle.
The present invention reduces the labor costs involved in enforcing
parking regulations. A single patrol vehicle can enforce
regulations over a much larger area than is possible when using
manual enforcement techniques (i.e. manually marking each vehicle's
tires and issuing hand written citations). The productivity gain
afforded by the system results in increased revenue for the
municipality from the same number of parking enforcement staff
Alternatively, the increased enforcement productivity can be used
to reduce the punitive fine levied for each parking infraction. The
computerized nature of the present invention supports efficient
data management through the court system without the requirement to
digitize hand-written citation forms. When linked to "electronic
banking" means, this centralized data management capability
supports the collection of timed "pay per use" fees for short term
parking, thereby emulating the functionality of parking meters.
Other Law Enforcement Applications
The data collected for the purpose of parking enforcement also has
a number of other law enforcement and public service applications.
Each vehicle observed by the system can be searched for in various
crime-related databases 56 and if a match is found, the appropriate
enforcement authorities can be called in to deal with the
situation. Examples of this embodiment are: 1) Identifying legally
parked vehicles that have been reported stolen. 2) Identifying
legally parked vehicles that should not be on the road (e.g.
vehicles registered to drivers whose license is under suspension).
3) Identifying legally parked vehicles whose owners have unpaid
public debts (e.g. unpaid traffic fines, child support payments,
etc.). 4) Identifying legally parked vehicles that are registered
to wanted fugitives. 5) Identifying legally parked vehicles whose
owners (convicted felons) may be violating their parole conditions
by being parked at a particular location at a particular time. 6)
Identifying legally parked vehicles having outstanding liens on
them that have resulted in judgements for repossession.
Embodiments of the invention that provide some or all of these
additional enforcement functions are similar to the parking control
embodiments described in detail above. The only functional
modification required to realize these embodiments is to link the
system to a database 56 of unique vehicle identifiers that meet the
desired law enforcement criteria. For example, if the parking
control vehicle drives by a (legally parked) vehicle that is
registered to a person having a large number of unpaid traffic
fines, the recognized plate-string would be matched to its entry on
the "wanted-list" maintained in the database. Any match to data
elements on the system's wanted-list would trigger a message to the
Enforcement Officer to take appropriate measures (e.g. call in a
tow truck that impounds the vehicle until the traffic fines have
been paid).
FIG. 10 is a flow-chart of a preferred embodiment that searches a
"wanted-list" of unique vehicle identifiers maintained in the
system's linked database 56. Each unique vehicle identifier is
entered on the list only after having been reasonably associated
with a person or persons wanted by law enforcement authorities.
Each data record stored in the system's linked database 56 also
contains instructions on what law enforcement actions are
appropriate to take in the event that the vehicle is located. Every
time the patrol vehicle 50 observes a new Epoch-ID, the wanted-list
is searched for a matching unique vehicle identifier 538. If a
match is found 539, then an alarm is sounded and a message is
displayed to the Officer providing instruction on what law
enforcement actions to take 540.
Typically, the "wanted-list" is updated at the end of each working
day when data is downloaded from the patrol vehicle for processing
through the Parking Authority's administrative system. However, if
there is an urgent requirement to locate the vehicle not already on
the wanted-list, that vehicle's unique identifier can be broadcast
over a wireless communication link to all parking patrol vehicles.
The new wanted vehicle is then immediately added to each patrol
vehicle's on-board wanted-list so that the Officers will be
automatically alerted if the wanted vehicle is encountered.
Embodiments that support the six enforcement functions listed above
can be implemented without the need for geo-referencing hardware 53
and time stamping hardware 55 (that hardware is only required to
detect parking violations or to determine the elapsed time that a
car has been legally parked at virtual-parking-meter). In a
preferred embodiment, the system operates in a "pure search mode"
rather than the "parking enforcement modes" described above. FIG.
10 illustrates the algorithm used when operating in the pure search
mode. The patrol vehicle makes no attempt to detect parking
violations and therefore does not need to return to the same
location at any prescribed time interval (i.e. step 500 need not be
directed along any pre-defined route). This embodiment must
uniquely identify each vehicle based on a single observation and
must therefore employ either Full Recognition Mode LPR, or an
active vehicle information means such as transponders.
Since parking regulations are not being enforced in pure search
mode, the plate-strings or transponders IDs recognized by the
system need not be geo-referenced or time-stamped. Patrol vehicles
operating in pure search mode simply drive about the municipality
identifying all parked vehicles. As each parked vehicle is
identified, it is searched for in the linked database's "wanted
list" 538. If a matching vehicle ID is found in the database 539,
then an alarm is sounded that alerts the driver to take the
pre-defined law enforcement action 540. This simpler embodiment of
the present invention would typically be mounted on public vehicles
that patrol streets in a random fashion during the course of
carrying out other public functions (e.g. police cars, ambulance,
road maintenance vehicles, etc.).
Embodiment That Insures the Privacy of Citizens
The present invention's ability to identify the whereabouts of
individual motorists raises concerns that the information could be
used to invade the privacy of citizens. Therefore, in a preferred
embodiment, all data collected during the course of enforcing
parking regulations is encrypted (shown as step 506 in FIGS. 5
through 10). The encryption algorithm used may be one of many that
are commonly used to assure the privacy and security of sensitive
information (e.g. financial transaction data transmitted over the
Internet). This encryption step insures that the data remains
unintelligible in the event that it falls into unauthorized hands.
For example, the Parking Enforcement Officers that use the system
should only be permitted to see data pertinent to vehicles that
have triggered a parking violation alarm. All other observed data
should remain unintelligible to the Officer and no unencrypted
digital data files should be downloadable from the system without
adequate security clearance. Access to unencrypted data is
restricted to duly appointed Public Authorities on a "need to know"
basis. Typically a court order would be required to gain access to
the de-encryption key need to search through Epoch-IDs that have
been observed by the system
* * * * *