U.S. patent application number 14/712384 was filed with the patent office on 2015-11-19 for systems and methods for detecting traffic signs.
This patent application is currently assigned to MOBILEYE VISION TECHNOLOGIES LTD.. The applicant listed for this patent is YAIR KAPACH, YOEL KRUPNIK, YOAV TAIEB. Invention is credited to YAIR KAPACH, YOEL KRUPNIK, YOAV TAIEB.
Application Number | 20150332104 14/712384 |
Document ID | / |
Family ID | 53277079 |
Filed Date | 2015-11-19 |
United States Patent
Application |
20150332104 |
Kind Code |
A1 |
KAPACH; YAIR ; et
al. |
November 19, 2015 |
SYSTEMS AND METHODS FOR DETECTING TRAFFIC SIGNS
Abstract
Systems and methods are provided for detecting traffic signs. In
one implementation, a traffic sign detection system for a vehicle
include at least one image capture device configured to acquire at
least one image of a scene including a traffic sign ahead of the
vehicle. The traffic sign detection system also includes a data
interface and at least one processing device programmed to receive
the at least one image via the data interface, transform the at
least one image, sample the transformed at least one image to
generate a plurality of images having different sizes, convolve
each of the plurality of images with a template image, compare each
pixel value of each convolved image to a predetermined threshold,
and select local maxima of pixel values within local regions of
each convolved image as attention candidates, the local maxima
being greater than the predetermined threshold.
Inventors: |
KAPACH; YAIR; (MODIIN,
IL) ; TAIEB; YOAV; (JERUSALEM, IL) ; KRUPNIK;
YOEL; (MEVASSERET ZION, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KAPACH; YAIR
TAIEB; YOAV
KRUPNIK; YOEL |
MODIIN
JERUSALEM
MEVASSERET ZION |
|
IL
IL
IL |
|
|
Assignee: |
MOBILEYE VISION TECHNOLOGIES
LTD.
JERUSALEM
IL
|
Family ID: |
53277079 |
Appl. No.: |
14/712384 |
Filed: |
May 14, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61993142 |
May 14, 2014 |
|
|
|
Current U.S.
Class: |
382/104 |
Current CPC
Class: |
G06K 9/6202 20130101;
G06K 9/6215 20130101; B60R 1/00 20130101; G06K 9/52 20130101; G06K
9/00818 20130101 |
International
Class: |
G06K 9/00 20060101
G06K009/00; G06K 9/52 20060101 G06K009/52; G06K 9/62 20060101
G06K009/62 |
Claims
1. A traffic sign detection system for a vehicle, comprising: at
least one image capture device configured to acquire at least one
image of an environment including a traffic sign ahead of the
vehicle; a data interface; and at least one processing device
programmed to: receive the at least one image via the data
interface; transform the at least one image; sample the transformed
at least one image to generate a plurality of images having
different sizes; convolve the plurality of images with a template
image; compare one or more pixel values of the convolved images to
a predetermined threshold; and select local maxima of pixel values
within local regions of the convolved images as attention
candidates, the local maxima being greater than the predetermined
threshold.
2. The traffic sign detection system of claim 1, further comprising
detecting the traffic sign based on the attention candidates.
3. The traffic sign detection system of claim 1, wherein the
traffic sign includes at least one of a triangular and rhombus
shape.
4. The traffic sign detection system of claim 1, wherein the at
least one processing device is programmed to transform the at least
one image by computing a distance between an angle formed by an
edge of the traffic sign and a predetermined angle.
5. The traffic sign detection system of claim 4, wherein the
predetermined angle is 60 degrees for detecting a triangular
shape.
6. The traffic sign detection system of claim 4, wherein the
predetermined angle is 45 degrees for detecting a rhombus
shape.
7. The traffic sign detection system of claim 4, wherein the at
least one processing device is programmed to transform the at least
one image by computing the distance based on a look up table.
8. The traffic sign detection system of claim 4, wherein the angle
formed by the edge of the traffic sign is computed from horizontal
and vertical gradients of each image point on the edge.
9. The traffic sign detection system of claim 8, wherein the
horizontal and vertical gradients of the edge are computed using a
nonlinear filter.
10. The traffic sign detection system of claim 9, wherein the
nonlinear filter is a Sobel filter.
11. The traffic sign detection system of claim 1, wherein the at
least one processing device is programmed to crop the at least one
image to reduce its size prior to transforming the at least one
image.
12. The traffic sign detection system of claim 1, wherein the
template image has a fixed size.
13. The traffic sign detection system of claim 1, wherein the
different sizes of the plurality of images vary linearly.
14. The traffic sign detection system of claim 1, wherein the
different sizes of the plurality of image vary logarithmically.
15. The traffic sign detection system of claim 1, wherein each of
the local regions has a predetermined size smaller than a size of
each convolved image.
16. The traffic sign detection system of claim 15, wherein the
predetermined size for each of the local regions is 5-pixel by
5-pixel.
17. The traffic sign detection system of claim 1, wherein the at
least one processing device is programmed to apply a non-maximum
suppression process to the convolved image after it is compared to
the threshold.
18. The traffic sign detection system of claim 17, wherein the
non-maximum suppression is applied within each of the local
regions.
19. A vehicle, comprising: a body; at least one image capture
device mounted on the body and configured to acquire at least one
image of an environment including a traffic sign ahead of the
vehicle; a data interface; and at least one processing device
programmed to: receive the at least one image via the data
interface; transform the at least one image; sample the transformed
at least one image to generate a plurality of images having
different sizes; convolve the plurality of images with a template
image; compare one or more pixel values of the convolved images to
a predetermined threshold; and select local maxima of pixel values
within local regions of the convolved image as attention
candidates, the local maxima being greater than the predetermined
threshold.
20. The vehicle of claim 19, wherein the at least one processing
device is further programmed to detect the traffic sign based on
the attention candidates.
21. The vehicle of claim 19, wherein the traffic sign includes at
least one of a triangular and rhombus shape.
22. The vehicle of claim 19, wherein the at least one processing
device is programmed to transform the at least one image by
computing a distance between an angle formed by an edge of the
traffic sign and a predetermined angle.
23. The vehicle of claim 22, wherein the angle formed by the edge
of the traffic sign is computed from horizontal and vertical
gradients of each image point on the edge.
24. The vehicle of claim 22, wherein the at least one processing
device is programmed to transform the at least one image by
computing the distance based on a look up table.
25. The vehicle of claim 19, wherein the template image has a fixed
size.
26. The vehicle of claim 19, wherein the different sizes of the
plurality of images vary linearly.
27. The vehicle of claim 19, wherein the different sizes of the
plurality of images vary logarithmically.
28. The vehicle of claim 19, wherein the at least one processing
device is programmed to apply a non-maximum suppression process to
the convolved image after it is compared to the threshold.
29. A method for detecting a traffic sign for a vehicle,
comprising: acquiring, via at least one image capture device, at
least one image of an environment including a traffic sign ahead of
the vehicle; receiving, via a processing unit, the at least one
image; transforming, via the processing unit, the at least one
image; sampling, via the processing unit, the transformed at least
one image to generate a plurality of images having different sizes;
convolving, via the processing unit, the plurality of images with a
template image; comparing, via the processing unit, one or more
pixel values of the convolved image to a predetermined threshold;
and selecting local maxima of pixel values within local regions of
the convolved image as attention candidates, the local maxima being
greater than the predetermined threshold.
30. The method of claim 29, further comprising detecting the
traffic sign based on the attention candidates.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of priority of U.S.
Provisional Patent Application No. 61/993,142, filed on May 14,
2014. The foregoing application is incorporated herein by reference
in its entirety.
BACKGROUND
[0002] I. Technical Field
[0003] The present disclosure relates generally to autonomous
vehicle navigation and, more specifically, to systems and methods
that use cameras to detect traffic signs.
[0004] II. Background Information
[0005] As technology continues to advance, the goal of a fully
autonomous vehicle that is capable of navigating on roadways is on
the horizon. Primarily, an autonomous vehicle may be able to
identify its environment and navigate without input from a human
operator. Autonomous vehicles may also take into account a variety
of factors and make appropriate decisions based on those factors to
safely and accurately reach an intended destination. For example,
various objects--such as other vehicles and pedestrians--are
encountered when a vehicle typically travels a roadway. Autonomous
driving systems may recognize these objects in a vehicle's
environment and take appropriate and timely action to avoid
collisions. Additionally, autonomous driving systems may identify
other indicators--such as traffic signals, traffic signs, and lane
markings--that regulate vehicle movement (e.g., when the vehicle
must stop and may go, a speed at which the vehicle must not exceed,
where the vehicle must be positioned on the roadway, etc.).
Autonomous driving systems may need to determine when a vehicle
should change lanes, turn at intersections, change roadways, etc.
As is evident from these examples, many factors may need to be
addressed in order to provide an autonomous vehicle that is capable
of navigating safely and accurately.
SUMMARY
[0006] Embodiments consistent with the present disclosure provide
systems and methods for autonomous vehicle navigation. The
disclosed embodiments may use cameras to provide autonomous vehicle
navigation features. For example, consistent with the disclosed
embodiments, the disclosed systems may include one, two, or more
cameras that monitor the environment of a vehicle and cause a
navigational response based on an analysis of images captured by
one or more of the cameras.
[0007] Consistent with a disclosed embodiment, a traffic sign
detection system for a vehicle is provided. The traffic sign
detection system may include at least one image capture device
configured to acquire at least one image of an environment
including a traffic sign ahead of the vehicle. The traffic sign
detection system may include a data interface, and at least one
processing device programmed to receive the at least one image via
the data interface. The at least one processing device may also be
programmed to transform the at least one image, and sample the
transformed at least one image to generate a plurality of images
having different sizes. The at least one processing device may also
be programmed to convolve the plurality of images with a template
image, and compare one or more pixel values of the convolved images
to a predetermined threshold. The at least one processing device
may also be programmed to select local maxima of pixel values
within local regions of the convolved images as attention
candidates, the local maxima being greater than the predetermined
threshold.
[0008] Consistent with another disclosed embodiment, a vehicle is
provided. The vehicle may include a body, and at least one image
capture device mounted on the body and configured to acquire at
least one image of an environment including a traffic sign ahead of
the vehicle. The vehicle may also include a data interface and at
least one processing device programmed to receive the at least one
image via the data interface. The at least one processing device
may also be programmed to transform the at least one image, and
sample the transformed at least one image to generate a plurality
of images having different sizes. The at least one processing
device may also be programmed to convolve the plurality of images
with a template image, and compare one or more pixel values of the
convolved images to a predetermined threshold. The at least one
processing device may also be programmed to select local maxima of
pixel values within local regions of the convolved images as
attention candidates, the local maxima being greater than the
predetermined threshold.
[0009] Consistent with yet another disclosed embodiment, a method
for detecting a traffic sign for a vehicle is provided. The method
may include acquiring at least one image of an environment
including a traffic sign ahead of the vehicle. The method may also
include receiving the at least one image, and transforming the at
least one image. The method may also include sampling the
transformed at least one image to generate a plurality of images
having different sizes. The method may also include convolving the
plurality of images a template image, and comparing one or more
pixel values of the convolved images to a predetermined threshold.
The method may also include selecting local maxima of pixel values
within local regions of the convolved images as attention
candidates, the local maxima being greater than the predetermined
threshold.
[0010] Consistent with other disclosed embodiments, non-transitory
computer-readable storage media may store program instructions,
which are executed by at least one processing device and perform
any of the methods described herein.
[0011] The foregoing general description and the following detailed
description are exemplary and explanatory only and are not
restrictive of the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The accompanying drawings, which are incorporated in and
constitute a part of this disclosure, illustrate various disclosed
embodiments. In the drawings:
[0013] FIG. 1 is a diagrammatic representation of an exemplary
system consistent with the disclosed embodiments.
[0014] FIG. 2A is a diagrammatic side view representation of an
exemplary vehicle including a system consistent with the disclosed
embodiments.
[0015] FIG. 2B is a diagrammatic top view representation of the
vehicle and system shown in FIG. 2A consistent with the disclosed
embodiments.
[0016] FIG. 2C is a diagrammatic top view representation of another
embodiment of a vehicle including a system consistent with the
disclosed embodiments.
[0017] FIG. 2D is a diagrammatic top view representation of yet
another embodiment of a vehicle including a system consistent with
the disclosed embodiments.
[0018] FIG. 2E is a diagrammatic top view representation of yet
another embodiment of a vehicle including a system consistent with
the disclosed embodiments.
[0019] FIG. 2F is a diagrammatic representation of exemplary
vehicle control systems consistent with the disclosed
embodiments.
[0020] FIG. 3A is a diagrammatic representation of an interior of a
vehicle including a rearview mirror and a user interface for a
vehicle imaging system consistent with the disclosed
embodiments.
[0021] FIG. 3B is an illustration of an example of a camera mount
that is configured to be positioned behind a rearview mirror and
against a vehicle windshield consistent with the disclosed
embodiments.
[0022] FIG. 3C is an illustration of the camera mount shown in FIG.
3B from a different perspective consistent with the disclosed
embodiments.
[0023] FIG. 3D is an illustration of an example of a camera mount
that is configured to be positioned behind a rearview mirror and
against a vehicle windshield consistent with the disclosed
embodiments.
[0024] FIG. 4 is an exemplary block diagram of a memory configured
to store instructions for performing one or more operations
consistent with the disclosed embodiments.
[0025] FIG. 5A is a flowchart showing an exemplary process for
causing one or more navigational responses based on monocular image
analysis consistent with disclosed embodiments.
[0026] FIG. 5B is a flowchart showing an exemplary process for
detecting one or more vehicles and/or pedestrians in a set of
images consistent with the disclosed embodiments.
[0027] FIG. 5C is a flowchart showing an exemplary process for
detecting road marks and/or lane geometry information in a set of
images consistent with the disclosed embodiments.
[0028] FIG. 5D is a flowchart showing an exemplary process for
detecting traffic lights in a set of images consistent with the
disclosed embodiments.
[0029] FIG. 5E is a flowchart showing an exemplary process for
causing one or more navigational responses based on a vehicle path
consistent with the disclosed embodiments.
[0030] FIG. 5F is a flowchart showing an exemplary process for
determining whether a leading vehicle is changing lanes consistent
with the disclosed embodiments.
[0031] FIG. 6 is a flowchart showing an exemplary process for
causing one or more navigational responses based on stereo image
analysis consistent with the disclosed embodiments.
[0032] FIG. 7 is a flowchart showing an exemplary process for
causing one or more navigational responses based on an analysis of
three sets of images consistent with the disclosed embodiments.
[0033] FIG. 8 shows an exemplary vehicle including a system for
detecting a traffic sign consistent with the disclosed
embodiments.
[0034] FIG. 9 shows exemplary traffic signs consistent with the
disclosed embodiments.
[0035] FIG. 10 is an exemplary block diagram of a memory that may
store instructions for performing one or more operations for
detecting traffic signs consistent with the disclosed
embodiments.
[0036] FIG. 11 shows an exemplary top level functional design of a
traffic sign recognition or detection system consistent with the
disclosed embodiments.
[0037] FIG. 12 is a flowchart showing an exemplary process for
efficiently detecting single-frame candidates consistent with the
disclosed embodiments.
[0038] FIG. 13A illustrates an exemplary result of an image after
being processed by an initial attention process consistent with the
disclosed embodiments.
[0039] FIG. 13B illustrates an exemplary result of an image after
being processed by a first rough classifier process consistent with
the disclosed embodiments.
[0040] FIG. 13C illustrates an exemplary result of an image after
being processed by a first alignment classifier process consistent
with the disclosed embodiments.
[0041] FIG. 13D illustrates an exemplary result of an image after
being processed by a second rough classifier process consistent
with the disclosed embodiments.
[0042] FIG. 13E illustrates an exemplary result of an image after
being processed by a second alignment classifier process consistent
with the disclosed embodiments.
[0043] FIG. 14 is a flowchart showing an exemplary process for
detecting a traffic sign consistent with the disclosed
embodiments.
[0044] FIG. 15A shows an exemplary image of a traffic sign at the
pixel level consistent with the disclosed embodiments.
[0045] FIG. 15B shows a relationship between a distance I.sub.x,y
and an angle .theta. consistent with the disclosed embodiments.
[0046] FIG. 16 shows an exemplary result of applying a distance to
theta transformation to an image consistent with the disclosed
embodiments.
[0047] FIG. 17 shows a pyramid of images generated by sampling a
transformed image consistent with the disclosed embodiments.
DETAILED DESCRIPTION
[0048] The following detailed description refers to the
accompanying drawings. Wherever possible, the same reference
numbers are used in the drawings and the following description to
refer to the same or similar parts. While several illustrative
embodiments are described herein, modifications, adaptations and
other implementations are possible. For example, substitutions,
additions or modifications may be made to the components
illustrated in the drawings, and the illustrative methods described
herein may be modified by substituting, reordering, removing, or
adding steps to the disclosed methods. Accordingly, the following
detailed description is not limited to the disclosed embodiments
and examples. Instead, the proper scope is defined by the appended
claims.
[0049] FIG. 1 is a block diagram representation of a system 100
consistent with the exemplary disclosed embodiments. System 100 may
include various components depending on the requirements of a
particular implementation. In some embodiments, system 100 may
include a processing unit 110, an image acquisition unit 120, a
position sensor 130, one or more memory units 140, 150, a map
database 160, and a user interface 170. Processing unit 110 may
include one or more processing devices. In some embodiments,
processing unit 110 may include an applications processor 180, an
image processor 190, or any other suitable processing device.
Similarly, image acquisition unit 120 may include any number of
image acquisition devices and components depending on the
requirements of a particular application. In some embodiments,
image acquisition unit 120 may include one or more image capture
devices (e.g., cameras), such as image capture device 122, image
capture device 124, and image capture device 126. System 100 may
also include a data interface 128 communicatively connecting
processing device 110 to image acquisition device 120. For example,
data interface 128 may include any wired and/or wireless link or
links for transmitting image data acquired by image accusation
device 120 to processing unit 110.
[0050] Both applications processor 180 and image processor 190 may
include various types of processing devices. For example, either or
both of applications processor 180 and image processor 190 may
include a microprocessor, preprocessors (such as an image
preprocessor), graphics processors, a central processing unit
(CPU), support circuits, digital signal processors, integrated
circuits, memory, or any other types of devices suitable for
running applications and for image processing and analysis. In some
embodiments, applications processor 180 and/or image processor 190
may include any type of single or multi-core processor, mobile
device microcontroller, central processing unit, etc. Various
processing devices may be used, including, for example, processors
available from manufacturers such as Intel.RTM., AMD.RTM., etc. and
may include various architectures (e.g., x86 processor, ARM.RTM.,
etc.).
[0051] In some embodiments, applications processor 180 and/or image
processor 190 may include any of the EyeQ series of processor chips
available from Mobileye.RTM.. These processor designs each include
multiple processing units with local memory and instruction sets.
Such processors may include video inputs for receiving image data
from multiple image sensors and may also include video out
capabilities. In one example, the EyeQ2.RTM. uses 90 nm-micron
technology operating at 332 Mhz. The EyeQ2.RTM. architecture
consists of two floating point, hyper-thread 32-bit RISC CPUs
(MIPS32.RTM. 34K.RTM. cores), five Vision Computing Engines (VCE),
three Vector Microcode Processors (VMP.RTM.), Denali 64-bit Mobile
DDR Controller, 128-bit internal Sonics Interconnect, dual 16-bit
Video input and 18-bit Video output controllers, 16 channels DMA
and several peripherals. The MIPS34K CPU manages the five VCEs,
three VMP.TM. and the DMA, the second MIPS34K CPU and the
multi-channel DMA as well as the other peripherals. The five VCEs,
three VMP.RTM. and the MIPS34K CPU can perform intensive vision
computations required by multi-function bundle applications. In
another example, the EyeQ3.RTM., which is a third generation
processor and is six times more powerful that the EyeQ2.RTM., may
be used in the disclosed embodiments.
[0052] Any of the processing devices disclosed herein may be
configured to perform certain functions. Configuring a processing
device, such as any of the described EyeQ processors or other
controller or microprocessor, to perform certain functions may
include programming of computer executable instructions and making
those instructions available to the processing device for execution
during operation of the processing device. In some embodiments,
configuring a processing device may include programming the
processing device directly with architectural instructions. In
other embodiments, configuring a processing device may include
storing executable instructions on a memory that is accessible to
the processing device during operation. For example, the processing
device may access the memory to obtain and execute the stored
instructions during operation.
[0053] While FIG. 1 depicts two separate processing devices
included in processing unit 110, more or fewer processing devices
may be used. For example, in some embodiments, a single processing
device may be used to accomplish the tasks of applications
processor 180 and image processor 190. In other embodiments, these
tasks may be performed by more than two processing devices.
[0054] Processing unit 110 may comprise various types of devices.
For example, processing unit 110 may include various devices, such
as a controller, an image preprocessor, a central processing unit
(CPU), support circuits, digital signal processors, integrated
circuits, memory, or any other types of devices for image
processing and analysis. The image preprocessor may include a video
processor for capturing, digitizing and processing the imagery from
the image sensors. The CPU may comprise any number of
microcontrollers or microprocessors. The support circuits may be
any number of circuits generally well known in the art, including
cache, power supply, clock and input-output circuits. The memory
may store software that, when executed by the processor, controls
the operation of the system. The memory may include databases and
image processing software. The memory may comprise any number of
random access memories, read only memories, flash memories, disk
drives, optical storage, tape storage, removable storage and other
types of storage. In one instance, the memory may be separate from
the processing unit 110. In another instance, the memory may be
integrated into the processing unit 110.
[0055] Each memory 140, 150 may include software instructions that
when executed by a processor (e.g., applications processor 180
and/or image processor 190), may control operation of various
aspects of system 100. These memory units may include various
databases and image processing software. The memory units may
include random access memory, read only memory, flash memory, disk
drives, optical storage, tape storage, removable storage and/or any
other types of storage. In some embodiments, memory units 140, 150
may be separate from the applications processor 180 and/or image
processor 190. In other embodiments, these memory units may be
integrated into applications processor 180 and/or image processor
190.
[0056] Position sensor 130 may include any type of device suitable
for determining a location associated with at least one component
of system 100. In some embodiments, position sensor 130 may include
a GPS receiver. Such receivers can determine a user position and
velocity by processing signals broadcasted by global positioning
system satellites. Position information from position sensor 130
may be made available to applications processor 180 and/or image
processor 190.
[0057] User interface 170 may include any device suitable for
providing information to or for receiving inputs from one or more
users of system 100. In some embodiments, user interface 170 may
include user input devices, including, for example, a touchscreen,
microphone, keyboard, pointer devices, track wheels, cameras,
knobs, buttons, etc. With such input devices, a user may be able to
provide information inputs or commands to system 100 by typing
instructions or information, providing voice commands, selecting
menu options on a screen using buttons, pointers, or eye-tracking
capabilities, or through any other suitable techniques for
communicating information to system 100.
[0058] User interface 170 may be equipped with one or more
processing devices configured to provide and receive information to
or from a user and process that information for use by, for
example, applications processor 180. In some embodiments, such
processing devices may execute instructions for recognizing and
tracking eye movements, receiving and interpreting voice commands,
recognizing and interpreting touches and/or gestures made on a
touchscreen, responding to keyboard entries or menu selections,
etc. In some embodiments, user interface 170 may include a display,
speaker, tactile device, and/or any other devices for providing
output information to a user.
[0059] Map database 160 may include any type of database for
storing map data useful to system 100. In some embodiments, map
database 160 may include data relating to the position, in a
reference coordinate system, of various items, including roads,
water features, geographic features, businesses, points of
interest, restaurants, gas stations, etc. Map database 160 may
store not only the locations of such items, but also descriptors
relating to those items, including, for example, names associated
with any of the stored features. In some embodiments, map database
160 may be physically located with other components of system 100.
Alternatively or additionally, map database 160 or a portion
thereof may be located remotely with respect to other components of
system 100 (e.g., processing unit 110). In such embodiments,
information from map database 160 may be downloaded over a wired or
wireless data connection to a network (e.g., over a cellular
network and/or the Internet, etc.).
[0060] Image capture devices 122, 124, and 126 may each include any
type of device suitable for capturing at least one image from an
environment. Moreover, any number of image capture devices may be
used to acquire images for input to the image processor. Some
embodiments may include only a single image capture device, while
other embodiments may include two, three, or even four or more
image capture devices. Image capture devices 122, 124, and 126 will
be further described with reference to FIGS. 2B-2E, below.
[0061] System 100, or various components thereof, may be
incorporated into various different platforms. In some embodiments,
system 100 may be included on a vehicle 200, as shown in FIG. 2A.
For example, vehicle 200 may be equipped with a processing unit 110
and any of the other components of system 100, as described above
relative to FIG. 1. While in some embodiments vehicle 200 may be
equipped with only a single image capture device (e.g., camera), in
other embodiments, such as those discussed in connection with FIGS.
2B-2E, multiple image capture devices may be used. For example,
either of image capture devices 122 and 124 of vehicle 200, as
shown in FIG. 2A, may be part of an ADAS (Advanced Driver
Assistance Systems) imaging set.
[0062] The image capture devices included on vehicle 200 as part of
the image acquisition unit 120 may be positioned at any suitable
location. In some embodiments, as shown in FIGS. 2A-2E and 3A-3C,
image capture device 122 may be located in the vicinity of the
rearview mirror. This position may provide a line of sight similar
to that of the driver of vehicle 200, which may aid in determining
what is and is not visible to the driver. Image capture device 122
may be positioned at any location near the rearview mirror, but
placing image capture device 122 on the driver side of the mirror
may further aid in obtaining images representative of the driver's
field of view and/or line of sight.
[0063] Other locations for the image capture devices of image
acquisition unit 120 may also be used. For example, image capture
device 124 may be located on or in a bumper of vehicle 200. Such a
location may be especially suitable for image capture devices
having a wide field of view. The line of sight of bumper-located
image capture devices can be different from that of the driver and,
therefore, the bumper image capture device and driver may not
always see the same objects. The image capture devices (e.g., image
capture devices 122, 124, and 126) may also be located in other
locations. For example, the image capture devices may be located on
or in one or both of the side mirrors of vehicle 200, on the roof
of vehicle 200, on the hood of vehicle 200, on the trunk of vehicle
200, on the sides of vehicle 200, mounted on, positioned behind, or
positioned in front of any of the windows of vehicle 200, and
mounted in or near light figures on the front and/or back of
vehicle 200, etc.
[0064] In addition to image capture devices, vehicle 200 may
include various other components of system 100. For example,
processing unit 110 may be included on vehicle 200 either
integrated with or separate from an engine control unit (ECU) of
the vehicle. Vehicle 200 may also be equipped with a position
sensor 130, such as a GPS receiver and may also include a map
database 160 and memory units 140 and 150.
[0065] FIG. 2A is a diagrammatic side view representation of an
exemplary vehicle imaging system consistent with the disclosed
embodiments. FIG. 2B is a diagrammatic top view illustration of the
embodiment shown in FIG. 2A. As illustrated in FIG. 2B, the
disclosed embodiments may include a vehicle 200 including in its
body a system 100 with a first image capture device 122 positioned
in the vicinity of the rearview mirror and/or near the driver of
vehicle 200, a second image capture device 124 positioned on or in
a bumper region (e.g., one of bumper regions 210) of vehicle 200,
and a processing unit 110.
[0066] As illustrated in FIG. 2C, image capture devices 122 and 124
may both be positioned in the vicinity of the rearview mirror
and/or near the driver of vehicle 200. Additionally, while two
image capture devices 122 and 124 are shown in FIGS. 2B and 2C, it
should be understood that other embodiments may include more than
two image capture devices. For example, in the embodiments shown in
FIGS. 2D and 2E, first, second, and third image capture devices
122, 124, and 126, are included in the system 100 of vehicle
200.
[0067] As illustrated in FIG. 2D, image capture device 122 may be
positioned in the vicinity of the rearview mirror and/or near the
driver of vehicle 200, and image capture devices 124 and 126 may be
positioned on or in a bumper region (e.g., one of bumper regions
210) of vehicle 200. And as shown in FIG. 2E, image capture devices
122, 124, and 126 may be positioned in the vicinity of the rearview
mirror and/or near the driver seat of vehicle 200. The disclosed
embodiments are not limited to any particular number and
configuration of the image capture devices, and the image capture
devices may be positioned in any appropriate location within and/or
on vehicle 200.
[0068] It is to be understood that the disclosed embodiments are
not limited to vehicles and could be applied in other contexts. It
is also to be understood that disclosed embodiments are not limited
to a particular type of vehicle 200 and may be applicable to all
types of vehicles including automobiles, trucks, trailers, and
other types of vehicles.
[0069] The first image capture device 122 may include any suitable
type of image capture device. Image capture device 122 may include
an optical axis. In one instance, the image capture device 122 may
include an Aptina M9V024 WVGA sensor with a global shutter. In
other embodiments, image capture device 122 may provide a
resolution of 1280.times.960 pixels and may include a rolling
shutter. Image capture device 122 may include various optical
elements. In some embodiments one or more lenses may be included,
for example, to provide a desired focal length and field of view
for the image capture device. In some embodiments, image capture
device 122 may be associated with a 6 mm lens or a 12 mm lens. In
some embodiments, image capture device 122 may be configured to
capture images having a desired field-of-view (FOV) 202, as
illustrated in FIG. 2D. For example, image capture device 122 may
be configured to have a regular FOV, such as within a range of 40
degrees to 56 degrees, including a 46 degree FOV, 50 degree FOV, 52
degree FOV, or greater. Alternatively, image capture device 122 may
be configured to have a narrow FOV in the range of 23 to 40
degrees, such as a 28 degree FOV or 36 degree FOV. In addition,
image capture device 122 may be configured to have a wide FOV in
the range of 100 to 180 degrees. In some embodiments, image capture
device 122 may include a wide angle bumper camera or one with up to
a 180 degree FOV.
[0070] The first image capture device 122 may acquire a plurality
of first images relative to an environment associated with the
vehicle 200. Each of the plurality of first images may be acquired
as a series of image scan lines, which may be captured using a
rolling shutter. Each scan line may include a plurality of
pixels.
[0071] The first image capture device 122 may have a scan rate
associated with acquisition of each of the first series of image
scan lines. The scan rate may refer to a rate at which an image
sensor can acquire image data associated with each pixel included
in a particular scan line.
[0072] Image capture devices 122, 124, and 126 may contain any
suitable type and number of image sensors, including CCD sensors or
CMOS sensors, for example. In one embodiment, a CMOS image sensor
may be employed along with a rolling shutter, such that each pixel
in a row is read one at a time, and scanning of the rows proceeds
on a row-by-row basis until an entire image frame has been
captured. In some embodiments, the rows may be captured
sequentially from top to bottom relative to the frame.
[0073] The use of a rolling shutter may result in pixels in
different rows being exposed and captured at different times, which
may cause skew and other image artifacts in the captured image
frame. On the other hand, when the image capture device 122 is
configured to operate with a global or synchronous shutter, all of
the pixels may be exposed for the same amount of time and during a
common exposure period. As a result, the image data in a frame
collected from a system employing a global shutter represents a
snapshot of the entire FOV (such as FOV 202) at a particular time.
In contrast, in a rolling shutter application, each row in a frame
is exposed and data is capture at different times. Thus, moving
objects may appear distorted in an image capture device having a
rolling shutter. This phenomenon will be described in greater
detail below.
[0074] The second image capture device 124 and the third image
capturing device 126 may be any type of image capture device. Like
the first image capture device 122, each of image capture devices
124 and 126 may include an optical axis. In one embodiment, each of
image capture devices 124 and 126 may include an Aptina M9V024 WVGA
sensor with a global shutter. Alternatively, each of image capture
devices 124 and 126 may include a rolling shutter. Like image
capture device 122, image capture devices 124 and 126 may be
configured to include various lenses and optical elements. In some
embodiments, lenses associated with image capture devices 124 and
126 may provide FOVs (such as FOVs 204 and 206) that are the same
as, or narrower than, a FOV (such as FOV 202) associated with image
capture device 122. For example, image capture devices 124 and 126
may have FOVs of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20
degrees, or less.
[0075] Image capture devices 124 and 126 may acquire a plurality of
second and third images relative to an environment associated with
the vehicle 200. Each of the plurality of second and third images
may be acquired as a second and third series of image scan lines,
which may be captured using a rolling shutter. Each scan line or
row may have a plurality of pixels. Image capture devices 124 and
126 may have second and third scan rates associated with
acquisition of each of image scan lines included in the second and
third series.
[0076] Each image capture device 122, 124, and 126 may be
positioned at any suitable position and orientation relative to
vehicle 200. The relative positioning of the image capture devices
122, 124, and 126 may be selected to aid in fusing together the
information acquired from the image capture devices. For example,
in some embodiments, a FOV (such as FOV 204) associated with image
capture device 124 may overlap partially or fully with a FOV (such
as FOV 202) associated with image capture device 122 and a FOV
(such as FOV 206) associated with image capture device 126.
[0077] Image capture devices 122, 124, and 126 may be located on
vehicle 200 at any suitable relative heights. In one instance,
there may be a height difference between the image capture devices
122, 124, and 126, which may provide sufficient parallax
information to enable stereo analysis. For example, as shown in
FIG. 2A, the two image capture devices 122 and 124 are at different
heights. There may also be a lateral displacement difference
between image capture devices 122, 124, and 126, giving additional
parallax information for stereo analysis by processing unit 110,
for example. The difference in the lateral displacement may be
denoted by d.sub.x, as shown in FIGS. 2C and 2D. In some
embodiments, fore or aft displacement (e.g., range displacement)
may exist between image capture devices 122, 124, and 126. For
example, image capture device 122 may be located 0.5 to 2 meters or
more behind image capture device 124 and/or image capture device
126. This type of displacement may enable one of the image capture
devices to cover potential blind spots of the other image capture
device(s).
[0078] Image capture devices 122 may have any suitable resolution
capability (e.g., number of pixels associated with the image
sensor), and the resolution of the image sensor(s) associated with
the image capture device 122 may be higher, lower, or the same as
the resolution of the image sensor(s) associated with image capture
devices 124 and 126. In some embodiments, the image sensor(s)
associated with image capture device 122 and/or image capture
devices 124 and 126 may have a resolution of 640.times.480,
1024.times.768, 1280.times.960, or any other suitable
resolution.
[0079] The frame rate (e.g., the rate at which an image capture
device acquires a set of pixel data of one image frame before
moving on to capture pixel data associated with the next image
frame) may be controllable. The frame rate associated with image
capture device 122 may be higher, lower, or the same as the frame
rate associated with image capture devices 124 and 126. The frame
rate associated with image capture devices 122, 124, and 126 may
depend on a variety of factors that may affect the timing of the
frame rate. For example, one or more of image capture devices 122,
124, and 126 may include a selectable pixel delay period imposed
before or after acquisition of image data associated with one or
more pixels of an image sensor in image capture device 122, 124,
and/or 126. Generally, image data corresponding to each pixel may
be acquired according to a clock rate for the device (e.g., one
pixel per clock cycle). Additionally, in embodiments including a
rolling shutter, one or more of image capture devices 122, 124, and
126 may include a selectable horizontal blanking period imposed
before or after acquisition of image data associated with a row of
pixels of an image sensor in image capture device 122, 124, and/or
126. Further, one or more of image capture devices 122, 124, and/or
126 may include a selectable vertical blanking period imposed
before or after acquisition of image data associated with an image
frame of image capture device 122, 124, and 126.
[0080] These timing controls may enable synchronization of frame
rates associated with image capture devices 122, 124, and 126, even
where the line scan rates of each are different. Additionally, as
will be discussed in greater detail below, these selectable timing
controls, among other factors (e.g., image sensor resolution,
maximum line scan rates, etc.) may enable synchronization of image
capture from an area where the FOV of image capture device 122
overlaps with one or more FOVs of image capture devices 124 and
126, even where the field of view of image capture device 122 is
different from the FOVs of image capture devices 124 and 126.
[0081] Frame rate timing in image capture device 122, 124, and 126
may depend on the resolution of the associated image sensors. For
example, assuming similar line scan rates for both devices, if one
device includes an image sensor having a resolution of
640.times.480 and another device includes an image sensor with a
resolution of 1280.times.960, then more time will be required to
acquire a frame of image data from the sensor having the higher
resolution.
[0082] Another factor that may affect the timing of image data
acquisition in image capture devices 122, 124, and 126 is the
maximum line scan rate. For example, acquisition of a row of image
data from an image sensor included in image capture device 122,
124, and 126 will require some minimum amount of time. Assuming no
pixel delay periods are added, this minimum amount of time for
acquisition of a row of image data will be related to the maximum
line scan rate for a particular device. Devices that offer higher
maximum line scan rates have the potential to provide higher frame
rates than devices with lower maximum line scan rates. In some
embodiments, one or more of image capture devices 124 and 126 may
have a maximum line scan rate that is higher than a maximum line
scan rate associated with image capture device 122. In some
embodiments, the maximum line scan rate of image capture device 124
and/or 126 may be 1.25, 1.5, 1.75, or 2 times or more than a
maximum line scan rate of image capture device 122.
[0083] In another embodiment, image capture devices 122, 124, and
126 may have the same maximum line scan rate, but image capture
device 122 may be operated at a scan rate less than or equal to its
maximum scan rate. The system may be configured such that one or
more of image capture devices 124 and 126 operate at a line scan
rate that is equal to the line scan rate of image capture device
122. In other instances, the system may be configured such that the
line scan rate of image capture device 124 and/or image capture
device 126 may be 1.25, 1.5, 1.75, or 2 times or more than the line
scan rate of image capture device 122.
[0084] In some embodiments, image capture devices 122, 124, and 126
may be asymmetric. That is, they may include cameras having
different fields of view (FOV) and focal lengths. The fields of
view of image capture devices 122, 124, and 126 may include any
desired area relative to an environment of vehicle 200, for
example. In some embodiments, one or more of image capture devices
122, 124, and 126 may be configured to acquire image data from an
environment in front of vehicle 200, behind vehicle 200, to the
sides of vehicle 200, or combinations thereof.
[0085] Further, the focal length associated with each image capture
device 122, 124, and/or 126 may be selectable (e.g., by inclusion
of appropriate lenses etc.) such that each device acquires images
of objects at a desired distance range relative to vehicle 200. For
example, in some embodiments image capture devices 122, 124, and
126 may acquire images of close-up objects within a few meters from
the vehicle. Image capture devices 122, 124, and 126 may also be
configured to acquire images of objects at ranges more distant from
the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). Further, the
focal lengths of image capture devices 122, 124, and 126 may be
selected such that one image capture device (e.g., image capture
device 122) can acquire images of objects relatively close to the
vehicle (e.g., within 10 m or within 20 m) while the other image
capture devices (e.g., image capture devices 124 and 126) can
acquire images of more distant objects (e.g., greater than 20 m, 50
m, 100 m, 150 m, etc.) from vehicle 200.
[0086] According to some embodiments, the FOV of one or more image
capture devices 122, 124, and 126 may have a wide angle. For
example, it may be advantageous to have a FOV of 140 degrees,
especially for image capture devices 122, 124, and 126 that may be
used to capture images of the area in the vicinity of vehicle 200.
For example, image capture device 122 may be used to capture images
of the area to the right or left of vehicle 200 and, in such
embodiments, it may be desirable for image capture device 122 to
have a wide FOV (e.g., at least 140 degrees).
[0087] The field of view associated with each of image capture
devices 122, 124, and 126 may depend on the respective focal
lengths. For example, as the focal length increases, the
corresponding field of view decreases.
[0088] Image capture devices 122, 124, and 126 may be configured to
have any suitable fields of view. In one particular example, image
capture device 122 may have a horizontal FOV of 46 degrees, image
capture device 124 may have a horizontal FOV of 23 degrees, and
image capture device 126 may have a horizontal FOV in between 23
and 46 degrees. In another instance, image capture device 122 may
have a horizontal FOV of 52 degrees, image capture device 124 may
have a horizontal FOV of 26 degrees, and image capture device 126
may have a horizontal FOV in between 26 and 52 degrees. In some
embodiments, a ratio of the FOV of image capture device 122 to the
FOVs of image capture device 124 and/or image capture device 126
may vary from 1.5 to 2.0. In other embodiments, this ratio may vary
between 1.25 and 2.25.
[0089] System 100 may be configured so that a field of view of
image capture device 122 overlaps, at least partially or fully,
with a field of view of image capture device 124 and/or image
capture device 126. In some embodiments, system 100 may be
configured such that the fields of view of image capture devices
124 and 126, for example, fall within (e.g., are narrower than) and
share a common center with the field of view of image capture
device 122. In other embodiments, the image capture devices 122,
124, and 126 may capture adjacent FOVs or may have partial overlap
in their FOVs. In some embodiments, the fields of view of image
capture devices 122, 124, and 126 may be aligned such that a center
of the narrower FOV image capture devices 124 and/or 126 may be
located in a lower half of the field of view of the wider FOV
device 122.
[0090] FIG. 2F is a diagrammatic representation of exemplary
vehicle control systems, consistent with the disclosed embodiments.
As indicated in FIG. 2F, vehicle 200 may include throttling system
220, braking system 230, and steering system 240. System 100 may
provide inputs (e.g., control signals) to one or more of throttling
system 220, braking system 230, and steering system 240 over one or
more data links (e.g., any wired and/or wireless link or links for
transmitting data). For example, based on analysis of images
acquired by image capture devices 122, 124, and/or 126, system 100
may provide control signals to one or more of throttling system
220, braking system 230, and steering system 240 to navigate
vehicle 200 (e.g., by causing an acceleration, a turn, a lane
shift, etc.). Further, system 100 may receive inputs from one or
more of throttling system 220, braking system 230, and steering
system 24 indicating operating conditions of vehicle 200 (e.g.,
speed, whether vehicle 200 is braking and/or turning, etc.).
Further details are provided in connection with FIGS. 4-7,
below.
[0091] As shown in FIG. 3A, vehicle 200 may also include a user
interface 170 for interacting with a driver or a passenger of
vehicle 200. For example, user interface 170 in a vehicle
application may include a touch screen 320, knobs 330, buttons 340,
and a microphone 350. A driver or passenger of vehicle 200 may also
use handles (e.g., located on or near the steering column of
vehicle 200 including, for example, turn signal handles), buttons
(e.g., located on the steering wheel of vehicle 200), and the like,
to interact with system 100. In some embodiments, microphone 350
may be positioned adjacent to a rearview mirror 310. Similarly, in
some embodiments, image capture device 122 may be located near
rearview mirror 310. In some embodiments, user interface 170 may
also include one or more speakers 360 (e.g., speakers of a vehicle
audio system). For example, system 100 may provide various
notifications (e.g., alerts) via speakers 360.
[0092] FIGS. 3B-3D are illustrations of an exemplary camera mount
370 configured to be positioned behind a rearview mirror (e.g.,
rearview mirror 310) and against a vehicle windshield, consistent
with disclosed embodiments. As shown in FIG. 3B, camera mount 370
may include image capture devices 122, 124, and 126. Image capture
devices 124 and 126 may be positioned behind a glare shield 380,
which may be flush against the vehicle windshield and include a
composition of film and/or anti-reflective materials. For example,
glare shield 380 may be positioned such that it aligns against a
vehicle windshield having a matching slope. In some embodiments,
each of image capture devices 122, 124, and 126 may be positioned
behind glare shield 380, as depicted, for example, in FIG. 3D. The
disclosed embodiments are not limited to any particular
configuration of image capture devices 122, 124, and 126, camera
mount 370, and glare shield 380. FIG. 3C is an illustration of
camera mount 370 shown in FIG. 3B from a front perspective.
[0093] As will be appreciated by a person skilled in the art having
the benefit of this disclosure, numerous variations and/or
modifications may be made to the foregoing disclosed embodiments.
For example, not all components are essential for the operation of
system 100. Further, any component may be located in any
appropriate part of system 100 and the components may be rearranged
into a variety of configurations while providing the functionality
of the disclosed embodiments. Therefore, the foregoing
configurations are examples and, regardless of the configurations
discussed above, system 100 can provide a wide range of
functionality to analyze the surroundings of vehicle 200 and
navigate vehicle 200 in response to the analysis.
[0094] As discussed below in further detail and consistent with
various disclosed embodiments, system 100 may provide a variety of
features related to autonomous driving and/or driver assist
technology. For example, system 100 may analyze image data,
position data (e.g., GPS location information), map data, speed
data, and/or data from sensors included in vehicle 200. System 100
may collect the data for analysis from, for example, image
acquisition unit 120, position sensor 130, and other sensors.
Further, system 100 may analyze the collected data to determine
whether or not vehicle 200 should take a certain action, and then
automatically take the determined action without human
intervention. For example, when vehicle 200 navigates without human
intervention, system 100 may automatically control the braking,
acceleration, and/or steering of vehicle 200 (e.g., by sending
control signals to one or more of throttling system 220, braking
system 230, and steering system 240). Further, system 100 may
analyze the collected data and issue warnings and/or alerts to
vehicle occupants based on the analysis of the collected data.
Additional details regarding the various embodiments that are
provided by system 100 are provided below.
[0095] Forward-Facing Multi-Imaging System
[0096] As discussed above, system 100 may provide drive assist
functionality that uses a multi-camera system. The multi-camera
system may use one or more cameras facing in the forward direction
of a vehicle. In other embodiments, the multi-camera system may
include one or more cameras facing to the side of a vehicle or to
the rear of the vehicle. In one embodiment, for example, system 100
may use a two-camera imaging system, where a first camera and a
second camera (e.g., image capture devices 122 and 124) may be
positioned at the front and/or the sides of a vehicle (e.g.,
vehicle 200). The first camera may have a field of view that is
greater than, less than, or partially overlapping with, the field
of view of the second camera. In addition, the first camera may be
connected to a first image processor to perform monocular image
analysis of images provided by the first camera, and the second
camera may be connected to a second image processor to perform
monocular image analysis of images provided by the second camera.
The outputs (e.g., processed information) of the first and second
image processors may be combined. In some embodiments, the second
image processor may receive images from both the first camera and
second camera to perform stereo analysis. In another embodiment,
system 100 may use a three-camera imaging system where each of the
cameras has a different field of view. Such a system may,
therefore, make decisions based on information derived from objects
located at varying distances both forward and to the sides of the
vehicle. References to monocular image analysis may refer to
instances where image analysis is performed based on images
captured from a single point of view (e.g., from a single camera).
Stereo image analysis may refer to instances where image analysis
is performed based on two or more images captured with one or more
variations of an image capture parameter. For example, captured
images suitable for performing stereo image analysis may include
images captured: from two or more different positions, from
different fields of view, using different focal lengths, along with
parallax information, etc.
[0097] For example, in one embodiment, system 100 may implement a
three camera configuration using image capture devices 122-126. In
such a configuration, image capture device 122 may provide a narrow
field of view (e.g., 34 degrees, or other values selected from a
range of about 20 to 45 degrees, etc.), image capture device 124
may provide a wide field of view (e.g., 150 degrees or other values
selected from a range of about 100 to about 180 degrees), and image
capture device 126 may provide an intermediate field of view (e.g.,
46 degrees or other values selected from a range of about 35 to
about 60 degrees). In some embodiments, image capture device 126
may act as a main or primary camera. Image capture devices 122-126
may be positioned behind rearview mirror 310 and positioned
substantially side-by-side (e.g., 6 cm apart). Further, in some
embodiments, as discussed above, one or more of image capture
devices 122-126 may be mounted behind glare shield 380 that is
flush with the windshield of vehicle 200. Such shielding may act to
minimize the impact of any reflections from inside the car on image
capture devices 122-126.
[0098] In another embodiment, as discussed above in connection with
FIGS. 3B and 3C, the wide field of view camera (e.g., image capture
device 124 in the above example) may be mounted lower than the
narrow and main field of view cameras (e.g., image devices 122 and
126 in the above example). This configuration may provide a free
line of sight from the wide field of view camera. To reduce
reflections, the cameras may be mounted close to the windshield of
vehicle 200, and may include polarizers on the cameras to damp
reflected light.
[0099] A three camera system may provide certain performance
characteristics. For example, some embodiments may include an
ability to validate the detection of objects by one camera based on
detection results from another camera. In the three camera
configuration discussed above, processing unit 110 may include, for
example, three processing devices (e.g., three EyeQ series of
processor chips, as discussed above), with each processing device
dedicated to processing images captured by one or more of image
capture devices 122-126.
[0100] In a three camera system, a first processing device may
receive images from both the main camera and the narrow field of
view camera, and perform vision processing of the narrow FOV camera
to, for example, detect other vehicles, pedestrians, lane marks,
traffic signs, traffic lights, and other road objects. Further, the
first processing device may calculate a disparity of pixels between
the images from the main camera and the narrow camera and create a
3D reconstruction of the environment of vehicle 200. The first
processing device may then combine the 3D reconstruction with 3D
map data or with 3D information calculated based on information
from another camera.
[0101] The second processing device may receive images from main
camera and perform vision processing to detect other vehicles,
pedestrians, lane marks, traffic signs, traffic lights, and other
road objects. Additionally, the second processing device may
calculate a camera displacement and, based on the displacement,
calculate a disparity of pixels between successive images and
create a 3D reconstruction of the environment (e.g., a structure
from motion). The second processing device may send the structure
from motion based 3D reconstruction to the first processing device
to be combined with the stereo 3D images.
[0102] The third processing device may receive images from the wide
FOV camera and process the images to detect vehicles, pedestrians,
lane marks, traffic signs, traffic lights, and other road objects.
The third processing device may further execute additional
processing instructions to analyze images to identify objects
moving in the image, such as vehicles changing lanes, pedestrians,
etc.
[0103] In some embodiments, having streams of image-based
information captured and processed independently may provide an
opportunity for providing redundancy in the system. Such redundancy
may include, for example, using a first image capture device and
the images processed from that device to validate and/or supplement
information obtained by capturing and processing image information
from at least a second image capture device.
[0104] In some embodiments, system 100 may use two image capture
devices (e.g., image capture devices 122 and 124) in providing
navigation assistance for vehicle 200 and use a third image capture
device (e.g., image capture device 126) to provide redundancy and
validate the analysis of data received from the other two image
capture devices. For example, in such a configuration, image
capture devices 122 and 124 may provide images for stereo analysis
by system 100 for navigating vehicle 200, while image capture
device 126 may provide images for monocular analysis by system 100
to provide redundancy and validation of information obtained based
on images captured from image capture device 122 and/or image
capture device 124. That is, image capture device 126 (and a
corresponding processing device) may be considered to provide a
redundant sub-system for providing a check on the analysis derived
from image capture devices 122 and 124 (e.g., to provide an
automatic emergency braking (AEB) system).
[0105] One of skill in the art will recognize that the above camera
configurations, camera placements, number of cameras, camera
locations, etc., are examples only. These components and others
described relative to the overall system may be assembled and used
in a variety of different configurations without departing from the
scope of the disclosed embodiments. Further details regarding usage
of a multi-camera system to provide driver assist and/or autonomous
vehicle functionality follow below.
[0106] FIG. 4 is an exemplary functional block diagram of memory
140 and/or 150, which may be stored/programmed with instructions
for performing one or more operations consistent with the disclosed
embodiments. Although the following refers to memory 140, one of
skill in the art will recognize that instructions may be stored in
memory 140 and/or 150.
[0107] As shown in FIG. 4, memory 140 may store a monocular image
analysis module 402, a stereo image analysis module 404, a velocity
and acceleration module 406, and a navigational response module
408. The disclosed embodiments are not limited to any particular
configuration of memory 140. Further, application processor 180
and/or image processor 190 may execute the instructions stored in
any of modules 402-408 included in memory 140. One of skill in the
art will understand that references in the following discussions to
processing unit 110 may refer to application processor 180 and
image processor 190 individually or collectively. Accordingly,
steps of any of the following processes may be performed by one or
more processing devices.
[0108] In one embodiment, monocular image analysis module 402 may
store instructions (such as computer vision software) which, when
executed by processing unit 110, performs monocular image analysis
of a set of images acquired by one of image capture devices 122,
124, and 126. In some embodiments, processing unit 110 may combine
information from a set of images with additional sensory
information (e.g., information from radar) to perform the monocular
image analysis. As described in connection with FIGS. 5A-5D below,
monocular image analysis module 402 may include instructions for
detecting a set of features within the set of images, such as lane
markings, vehicles, pedestrians, road signs, highway exit ramps,
traffic lights, hazardous objects, and any other feature associated
with an environment of a vehicle. Based on the analysis, system 100
(e.g., via processing unit 110) may cause one or more navigational
responses in vehicle 200, such as a turn, a lane shift, a change in
acceleration, and the like, as discussed below in connection with
navigational response module 408.
[0109] In one embodiment, stereo image analysis module 404 may
store instructions (such as computer vision software) which, when
executed by processing unit 110, performs stereo image analysis of
first and second sets of images acquired by a combination of image
capture devices selected from any of image capture devices 122,
124, and 126. In some embodiments, processing unit 110 may combine
information from the first and second sets of images with
additional sensory information (e.g., information from radar) to
perform the stereo image analysis. For example, stereo image
analysis module 404 may include instructions for performing stereo
image analysis based on a first set of images acquired by image
capture device 124 and a second set of images acquired by image
capture device 126. As described in connection with FIG. 6 below,
stereo image analysis module 404 may include instructions for
detecting a set of features within the first and second sets of
images, such as lane markings, vehicles, pedestrians, road signs,
highway exit ramps, traffic lights, hazardous objects, and the
like. Based on the analysis, processing unit 110 may cause one or
more navigational responses in vehicle 200, such as a turn, a lane
shift, a change in acceleration, and the like, as discussed below
in connection with navigational response module 408.
[0110] In one embodiment, velocity and acceleration module 406 may
store software configured to analyze data received from one or more
computing and electromechanical devices in vehicle 200 that are
configured to cause a change in velocity and/or acceleration of
vehicle 200. For example, processing unit 110 may execute
instructions associated with velocity and acceleration module 406
to calculate a target speed for vehicle 200 based on data derived
from execution of monocular image analysis module 402 and/or stereo
image analysis module 404. Such data may include, for example, a
target position, velocity, and/or acceleration, the position and/or
speed of vehicle 200 relative to a nearby vehicle, pedestrian, or
road object, position information for vehicle 200 relative to lane
markings of the road, and the like. In addition, processing unit
110 may calculate a target speed for vehicle 200 based on sensory
input (e.g., information from radar) and input from other systems
of vehicle 200, such as throttling system 220, braking system 230,
and/or steering system 240 of vehicle 200. Based on the calculated
target speed, processing unit 110 may transmit electronic signals
to throttling system 220, braking system 230, and/or steering
system 240 of vehicle 200 to trigger a change in velocity and/or
acceleration by, for example, physically depressing the brake or
easing up off the accelerator of vehicle 200.
[0111] In one embodiment, navigational response module 408 may
store software executable by processing unit 110 to determine a
desired navigational response based on data derived from execution
of monocular image analysis module 402 and/or stereo image analysis
module 404. Such data may include position and speed information
associated with nearby vehicles, pedestrians, and road objects,
target position information for vehicle 200, and the like.
Additionally, in some embodiments, the navigational response may be
based (partially or fully) on map data, a predetermined position of
vehicle 200, and/or a relative velocity or a relative acceleration
between vehicle 200 and one or more objects detected from execution
of monocular image analysis module 402 and/or stereo image analysis
module 404. Navigational response module 408 may also determine a
desired navigational response based on sensory input (e.g.,
information from radar) and inputs from other systems of vehicle
200, such as throttling system 220, braking system 230, and
steering system 240 of vehicle 200. Based on the desired
navigational response, processing unit 110 may transmit electronic
signals to throttling system 220, braking system 230, and steering
system 240 of vehicle 200 to trigger a desired navigational
response by, for example, turning the steering wheel of vehicle 200
to achieve a rotation of a predetermined angle. In some
embodiments, processing unit 110 may use the output of navigational
response module 408 (e.g., the desired navigational response) as an
input to execution of velocity and acceleration module 406 for
calculating a change in speed of vehicle 200.
[0112] FIG. 5A is a flowchart showing an exemplary process 500A for
causing one or more navigational responses based on monocular image
analysis, consistent with disclosed embodiments. At step 510,
processing unit 110 may receive a plurality of images via data
interface 128 between processing unit 110 and image acquisition
unit 120. For instance, a camera included in image acquisition unit
120 (such as image capture device 122 having field of view 202) may
capture a plurality of images of an area forward of vehicle 200 (or
to the sides or rear of a vehicle, for example) and transmit them
over a data connection (e.g., digital, wired, USB, wireless,
Bluetooth, etc.) to processing unit 110. Processing unit 110 may
execute monocular image analysis module 402 to analyze the
plurality of images at step 520, as described in further detail in
connection with FIGS. 5B-5D below. By performing the analysis,
processing unit 110 may detect a set of features within the set of
images, such as lane markings, vehicles, pedestrians, road signs,
highway exit ramps, traffic lights, and the like.
[0113] Processing unit 110 may also execute monocular image
analysis module 402 to detect various road hazards at step 520,
such as, for example, parts of a truck tire, fallen road signs,
loose cargo, small animals, and the like. Road hazards may vary in
structure, shape, size, and color, which may make detection of such
hazards more challenging. In some embodiments, processing unit 110
may execute monocular image analysis module 402 to perform
multi-frame analysis on the plurality of images to detect road
hazards. For example, processing unit 110 may estimate camera
motion between consecutive image frames and calculate the
disparities in pixels between the frames to construct a 3D-map of
the road. Processing unit 110 may then use the 3D-map to detect the
road surface, as well as hazards existing above the road
surface.
[0114] At step 530, processing unit 110 may execute navigational
response module 408 to cause one or more navigational responses in
vehicle 200 based on the analysis performed at step 520 and the
techniques as described above in connection with FIG. 4.
Navigational responses may include, for example, a turn, a lane
shift, a change in acceleration, and the like. In some embodiments,
processing unit 110 may use data derived from execution of velocity
and acceleration module 406 to cause the one or more navigational
responses. Additionally, multiple navigational responses may occur
simultaneously, in sequence, or any combination thereof. For
instance, processing unit 110 may cause vehicle 200 to shift one
lane over and then accelerate by, for example, sequentially
transmitting control signals to steering system 240 and throttling
system 220 of vehicle 200. Alternatively, processing unit 110 may
cause vehicle 200 to brake while at the same time shifting lanes
by, for example, simultaneously transmitting control signals to
braking system 230 and steering system 240 of vehicle 200.
[0115] FIG. 5B is a flowchart showing an exemplary process 500B for
detecting one or more vehicles and/or pedestrians in a set of
images, consistent with disclosed embodiments. Processing unit 110
may execute monocular image analysis module 402 to implement
process 500B. At step 540, processing unit 110 may determine a set
of candidate objects representing possible vehicles and/or
pedestrians. For example, processing unit 110 may scan one or more
images, compare the images to one or more predetermined patterns,
and identify within each image possible locations that may contain
objects of interest (e.g., vehicles, pedestrians, or portions
thereof). The predetermined patterns may be designed in such a way
to achieve a high rate of "false hits" and a low rate of "misses."
For example, processing unit 110 may use a low threshold of
similarity to predetermined patterns for identifying candidate
objects as possible vehicles or pedestrians. Doing so may allow
processing unit 110 to reduce the probability of missing (e.g., not
identifying) a candidate object representing a vehicle or
pedestrian.
[0116] At step 542, processing unit 110 may filter the set of
candidate objects to exclude certain candidates (e.g., irrelevant
or less relevant objects) based on classification criteria. Such
criteria may be derived from various properties associated with
object types stored in a database (e.g., a database stored in
memory 140). Properties may include object shape, dimensions,
texture, position (e.g., relative to vehicle 200), and the like.
Thus, processing unit 110 may use one or more sets of criteria to
reject false candidates from the set of candidate objects.
[0117] At step 544, processing unit 110 may analyze multiple frames
of images to determine whether objects in the set of candidate
objects represent vehicles and/or pedestrians. For example,
processing unit 110 may track a detected candidate object across
consecutive frames and accumulate frame-by-frame data associated
with the detected object (e.g., size, position relative to vehicle
200, etc.). Additionally, processing unit 110 may estimate
parameters for the detected object and compare the object's
frame-by-frame position data to a predicted position.
[0118] At step 546, processing unit 110 may construct a set of
measurements for the detected objects. Such measurements may
include, for example, position, velocity, and acceleration values
(relative to vehicle 200) associated with the detected objects. In
some embodiments, processing unit 110 may construct the
measurements based on estimation techniques using a series of
time-based observations such as Kalman filters or linear quadratic
estimation (LQE), and/or based on available modeling data for
different object types (e.g., cars, trucks, pedestrians, bicycles,
road signs, etc.). The Kalman filters may be based on a measurement
of an object's scale, where the scale measurement is proportional
to a time to collision (e.g., the amount of time for vehicle 200 to
reach the object). Thus, by performing steps 540-546, processing
unit 110 may identify vehicles and pedestrians appearing within the
set of captured images and derive information (e.g., position,
speed, size) associated with the vehicles and pedestrians. Based on
the identification and the derived information, processing unit 110
may cause one or more navigational responses in vehicle 200, as
described in connection with FIG. 5A, above.
[0119] At step 548, processing unit 110 may perform an optical flow
analysis of one or more images to reduce the probabilities of
detecting a "false hit" and missing a candidate object that
represents a vehicle or pedestrian. The optical flow analysis may
refer to, for example, analyzing motion patterns relative to
vehicle 200 in the one or more images associated with other
vehicles and pedestrians, and that are distinct from road surface
motion. Processing unit 110 may calculate the motion of candidate
objects by observing the different positions of the objects across
multiple image frames, which are captured at different times.
Processing unit 110 may use the position and time values as inputs
into mathematical models for calculating the motion of the
candidate objects. Thus, optical flow analysis may provide another
method of detecting vehicles and pedestrians that are nearby
vehicle 200. Processing unit 110 may perform optical flow analysis
in combination with steps 540-546 to provide redundancy for
detecting vehicles and pedestrians and increase the reliability of
system 100.
[0120] FIG. 5C is a flowchart showing an exemplary process 500C for
detecting road marks and/or lane geometry information in a set of
images, consistent with disclosed embodiments. Processing unit 110
may execute monocular image analysis module 402 to implement
process 500C. At step 550, processing unit 110 may detect a set of
objects by scanning one or more images. To detect segments of lane
markings, lane geometry information, and other pertinent road
marks, processing unit 110 may filter the set of objects to exclude
those determined to be irrelevant (e.g., minor potholes, small
rocks, etc.). At step 552, processing unit 110 may group together
the segments detected in step 550 belonging to the same road mark
or lane mark. Based on the grouping, processing unit 110 may
develop a model to represent the detected segments, such as a
mathematical model.
[0121] At step 554, processing unit 110 may construct a set of
measurements associated with the detected segments. In some
embodiments, processing unit 110 may create a projection of the
detected segments from the image plane onto the real-world plane.
The projection may be characterized using a 3rd-degree polynomial
having coefficients corresponding to physical properties such as
the position, slope, curvature, and curvature derivative of the
detected road. In generating the projection, processing unit 110
may take into account changes in the road surface, as well as pitch
and roll rates associated with vehicle 200. In addition, processing
unit 110 may model the road elevation by analyzing position and
motion cues present on the road surface. Further, processing unit
110 may estimate the pitch and roll rates associated with vehicle
200 by tracking a set of feature points in the one or more
images.
[0122] At step 556, processing unit 110 may perform multi-frame
analysis by, for example, tracking the detected segments across
consecutive image frames and accumulating frame-by-frame data
associated with detected segments. As processing unit 110 performs
multi-frame analysis, the set of measurements constructed at step
554 may become more reliable and associated with an increasingly
higher confidence level. Thus, by performing steps 550-556,
processing unit 110 may identify road marks appearing within the
set of captured images and derive lane geometry information. Based
on the identification and the derived information, processing unit
110 may cause one or more navigational responses in vehicle 200, as
described in connection with FIG. 5A, above.
[0123] At step 558, processing unit 110 may consider additional
sources of information to further develop a safety model for
vehicle 200 in the context of its surroundings. Processing unit 110
may use the safety model to define a context in which system 100
may execute autonomous control of vehicle 200 in a safe manner. To
develop the safety model, in some embodiments, processing unit 110
may consider the position and motion of other vehicles, the
detected road edges and barriers, and/or general road shape
descriptions extracted from map data (such as data from map
database 160). By considering additional sources of information,
processing unit 110 may provide redundancy for detecting road marks
and lane geometry and increase the reliability of system 100.
[0124] FIG. 5D is a flowchart showing an exemplary process 500D for
detecting traffic lights in a set of images, consistent with
disclosed embodiments. Processing unit 110 may execute monocular
image analysis module 402 to implement process 500D. At step 560,
processing unit 110 may scan the set of images and identify objects
appearing at locations in the images likely to contain traffic
lights. For example, processing unit 110 may filter the identified
objects to construct a set of candidate objects, excluding those
objects unlikely to correspond to traffic lights. The filtering may
be done based on various properties associated with traffic lights,
such as shape, dimensions, texture, position (e.g., relative to
vehicle 200), and the like. Such properties may be based on
multiple examples of traffic lights and traffic control signals and
stored in a database. In some embodiments, processing unit 110 may
perform multi-frame analysis on the set of candidate objects
reflecting possible traffic lights. For example, processing unit
110 may track the candidate objects across consecutive image
frames, estimate the real-world position of the candidate objects,
and filter out those objects that are moving (which are unlikely to
be traffic lights). In some embodiments, processing unit 110 may
perform color analysis on the candidate objects and identify the
relative position of the detected colors appearing inside possible
traffic lights.
[0125] At step 562, processing unit 110 may analyze the geometry of
a junction. The analysis may be based on any combination of: (i)
the number of lanes detected on either side of vehicle 200, (ii)
markings (such as arrow marks) detected on the road, and (iii)
descriptions of the junction extracted from map data (such as data
from map database 160). Processing unit 110 may conduct the
analysis using information derived from execution of monocular
analysis module 402. In addition, Processing unit 110 may determine
a correspondence between the traffic lights detected at step 560
and the lanes appearing near vehicle 200.
[0126] As vehicle 200 approaches the junction, at step 564,
processing unit 110 may update the confidence level associated with
the analyzed junction geometry and the detected traffic lights. For
instance, the number of traffic lights estimated to appear at the
junction as compared with the number actually appearing at the
junction may impact the confidence level. Thus, based on the
confidence level, processing unit 110 may delegate control to the
driver of vehicle 200 in order to improve safety conditions. By
performing steps 560-564, processing unit 110 may identify traffic
lights appearing within the set of captured images and analyze
junction geometry information. Based on the identification and the
analysis, processing unit 110 may cause one or more navigational
responses in vehicle 200, as described in connection with FIG. 5A,
above.
[0127] FIG. 5E is a flowchart showing an exemplary process 500E for
causing one or more navigational responses in vehicle 200 based on
a vehicle path, consistent with the disclosed embodiments. At step
570, processing unit 110 may construct an initial vehicle path
associated with vehicle 200. The vehicle path may be represented
using a set of points expressed in coordinates (x, z), and the
distance d.sub.i between two points in the set of points may fall
in the range of 1 to 5 meters. In one embodiment, processing unit
110 may construct the initial vehicle path using two polynomials,
such as left and right road polynomials. Processing unit 110 may
calculate the geometric midpoint between the two polynomials and
offset each point included in the resultant vehicle path by a
predetermined offset (e.g., a smart lane offset), if any (an offset
of zero may correspond to travel in the middle of a lane). The
offset may be in a direction perpendicular to a segment between any
two points in the vehicle path. In another embodiment, processing
unit 110 may use one polynomial and an estimated lane width to
offset each point of the vehicle path by half the estimated lane
width plus a predetermined offset (e.g., a smart lane offset).
[0128] At step 572, processing unit 110 may update the vehicle path
constructed at step 570. Processing unit 110 may reconstruct the
vehicle path constructed at step 570 using a higher resolution,
such that the distance d.sub.k between two points in the set of
points representing the vehicle path is less than the distance
d.sub.i described above. For example, the distance d.sub.k may fall
in the range of 0.1 to 0.3 meters. Processing unit 110 may
reconstruct the vehicle path using a parabolic spline algorithm,
which may yield a cumulative distance vector S corresponding to the
total length of the vehicle path (i.e., based on the set of points
representing the vehicle path).
[0129] At step 574, processing unit 110 may determine a look-ahead
point (expressed in coordinates as (x.sub.l, z.sub.l)) based on the
updated vehicle path constructed at step 572. Processing unit 110
may extract the look-ahead point from the cumulative distance
vector S, and the look-ahead point may be associated with a
look-ahead distance and look-ahead time. The look-ahead distance,
which may have a lower bound ranging from 10 to 20 meters, may be
calculated as the product of the speed of vehicle 200 and the
look-ahead time. For example, as the speed of vehicle 200
decreases, the look-ahead distance may also decrease (e.g., until
it reaches the lower bound). The look-ahead time, which may range
from 0.5 to 1.5 seconds, may be inversely proportional to the gain
of one or more control loops associated with causing a navigational
response in vehicle 200, such as the heading error tracking control
loop. For example, the gain of the heading error tracking control
loop may depend on the bandwidth of a yaw rate loop, a steering
actuator loop, car lateral dynamics, and the like. Thus, the higher
the gain of the heading error tracking control loop, the lower the
look-ahead time.
[0130] At step 576, processing unit 110 may determine a heading
error and yaw rate command based on the look-ahead point determined
at step 574. Processing unit 110 may determine the heading error by
calculating the arctangent of the look-ahead point, e.g., arctan
(x.sub.l/z.sub.l). Processing unit 110 may determine the yaw rate
command as the product of the heading error and a high-level
control gain. The high-level control gain may be equal to:
(2/look-ahead time), if the look-ahead distance is not at the lower
bound. Otherwise, the high-level control gain may be equal to:
(2*speed of vehicle 200/look-ahead distance).
[0131] FIG. 5F is a flowchart showing an exemplary process 500F for
determining whether a leading vehicle is changing lanes, consistent
with the disclosed embodiments. At step 580, processing unit 110
may determine navigation information associated with a leading
vehicle (e.g., a vehicle traveling ahead of vehicle 200). For
example, processing unit 110 may determine the position, velocity
(e.g., direction and speed), and/or acceleration of the leading
vehicle, using the techniques described in connection with FIGS. 5A
and 5B, above. Processing unit 110 may also determine one or more
road polynomials, a look-ahead point (associated with vehicle 200),
and/or a snail trail (e.g., a set of points describing a path taken
by the leading vehicle), using the techniques described in
connection with FIG. 5E, above.
[0132] At step 582, processing unit 110 may analyze the navigation
information determined at step 580. In one embodiment, processing
unit 110 may calculate the distance between a snail trail and a
road polynomial (e.g., along the trail). If the variance of this
distance along the trail exceeds a predetermined threshold (for
example, 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on
a moderately curvy road, and 0.5 to 0.6 meters on a road with sharp
curves), processing unit 110 may determine that the leading vehicle
is likely changing lanes. In the case where multiple vehicles are
detected traveling ahead of vehicle 200, processing unit 110 may
compare the snail trails associated with each vehicle. Based on the
comparison, processing unit 110 may determine that a vehicle whose
snail trail does not match with the snail trails of the other
vehicles is likely changing lanes. Processing unit 110 may
additionally compare the curvature of the snail trail (associated
with the leading vehicle) with the expected curvature of the road
segment in which the leading vehicle is traveling. The expected
curvature may be extracted from map data (e.g., data from map
database 160), from road polynomials, from other vehicles' snail
trails, from prior knowledge about the road, and the like. If the
difference in curvature of the snail trail and the expected
curvature of the road segment exceeds a predetermined threshold,
processing unit 110 may determine that the leading vehicle is
likely changing lanes.
[0133] In another embodiment, processing unit 110 may compare the
leading vehicle's instantaneous position with the look-ahead point
(associated with vehicle 200) over a specific period of time (e.g.,
0.5 to 1.5 seconds). If the distance between the leading vehicle's
instantaneous position and the look-ahead point varies during the
specific period of time, and the cumulative sum of variation
exceeds a predetermined threshold (for example, 0.3 to 0.4 meters
on a straight road, 0.7 to 0.8 meters on a moderately curvy road,
and 1.3 to 1.7 meters on a road with sharp curves), processing unit
110 may determine that the leading vehicle is likely changing
lanes. In another embodiment, processing unit 110 may analyze the
geometry of the snail trail by comparing the lateral distance
traveled along the trail with the expected curvature of the snail
trail. The expected radius of curvature may be determined according
to the calculation:
(.delta..sub.z.sup.2+.delta..sub.x.sup.2)/2/(.delta..sub.x), where
.delta..sub.x represents the lateral distance traveled and
.delta..sub.z represents the longitudinal distance traveled. If the
difference between the lateral distance traveled and the expected
curvature exceeds a predetermined threshold (e.g., 500 to 700
meters), processing unit 110 may determine that the leading vehicle
is likely changing lanes. In another embodiment, processing unit
110 may analyze the position of the leading vehicle. If the
position of the leading vehicle obscures a road polynomial (e.g.,
the leading vehicle is overlaid on top of the road polynomial),
then processing unit 110 may determine that the leading vehicle is
likely changing lanes. In the case where the position of the
leading vehicle is such that, another vehicle is detected ahead of
the leading vehicle and the snail trails of the two vehicles are
not parallel, processing unit 110 may determine that the (closer)
leading vehicle is likely changing lanes.
[0134] At step 584, processing unit 110 may determine whether or
not leading vehicle 200 is changing lanes based on the analysis
performed at step 582. For example, processing unit 110 may make
the determination based on a weighted average of the individual
analyses performed at step 582. Under such a scheme, for example, a
decision by processing unit 110 that the leading vehicle is likely
changing lanes based on a particular type of analysis may be
assigned a value of "1" (and "0" to represent a determination that
the leading vehicle is not likely changing lanes). Different
analyses performed at step 582 may be assigned different weights,
and the disclosed embodiments are not limited to any particular
combination of analyses and weights.
[0135] FIG. 6 is a flowchart showing an exemplary process 600 for
causing one or more navigational responses based on stereo image
analysis, consistent with disclosed embodiments. At step 610,
processing unit 110 may receive a first and second plurality of
images via data interface 128. For example, cameras included in
image acquisition unit 120 (such as image capture devices 122 and
124 having fields of view 202 and 204) may capture a first and
second plurality of images of an area forward of vehicle 200 and
transmit them over a digital connection (e.g., USB, wireless,
Bluetooth, etc.) to processing unit 110. In some embodiments,
processing unit 110 may receive the first and second plurality of
images via two or more data interfaces. The disclosed embodiments
are not limited to any particular data interface configurations or
protocols.
[0136] At step 620, processing unit 110 may execute stereo image
analysis module 404 to perform stereo image analysis of the first
and second plurality of images to create a 3D map of the road in
front of the vehicle and detect features within the images, such as
lane markings, vehicles, pedestrians, road signs, highway exit
ramps, traffic lights, road hazards, and the like. Stereo image
analysis may be performed in a manner similar to the steps
described in connection with FIGS. 5A-5D, above. For example,
processing unit 110 may execute stereo image analysis module 404 to
detect candidate objects (e.g., vehicles, pedestrians, road marks,
traffic lights, road hazards, etc.) within the first and second
plurality of images, filter out a subset of the candidate objects
based on various criteria, and perform multi-frame analysis,
construct measurements, and determine a confidence level for the
remaining candidate objects. In performing the steps above,
processing unit 110 may consider information from both the first
and second plurality of images, rather than information from one
set of images alone. For example, processing unit 110 may analyze
the differences in pixel-level data (or other data subsets from
among the two streams of captured images) for a candidate object
appearing in both the first and second plurality of images. As
another example, processing unit 110 may estimate a position and/or
velocity of a candidate object (e.g., relative to vehicle 200) by
observing that the object appears in one of the plurality of images
but not the other or relative to other differences that may exist
relative to objects appearing if the two image streams. For
example, position, velocity, and/or acceleration relative to
vehicle 200 may be determined based on trajectories, positions,
movement characteristics, etc. of features associated with an
object appearing in one or both of the image streams.
[0137] At step 630, processing unit 110 may execute navigational
response module 408 to cause one or more navigational responses in
vehicle 200 based on the analysis performed at step 620 and the
techniques as described above in connection with FIG. 4.
Navigational responses may include, for example, a turn, a lane
shift, a change in acceleration, a change in velocity, braking, and
the like. In some embodiments, processing unit 110 may use data
derived from execution of velocity and acceleration module 406 to
cause the one or more navigational responses. Additionally,
multiple navigational responses may occur simultaneously, in
sequence, or any combination thereof.
[0138] FIG. 7 is a flowchart showing an exemplary process 700 for
causing one or more navigational responses based on an analysis of
three sets of images, consistent with disclosed embodiments. At
step 710, processing unit 110 may receive a first, second, and
third plurality of images via data interface 128. For instance,
cameras included in image acquisition unit 120 (such as image
capture devices 122, 124, and 126 having fields of view 202, 204,
and 206) may capture a first, second, and third plurality of images
of an area forward and/or to the side of vehicle 200 and transmit
them over a digital connection (e.g., USB, wireless, Bluetooth,
etc.) to processing unit 110. In some embodiments, processing unit
110 may receive the first, second, and third plurality of images
via three or more data interfaces. For example, each of image
capture devices 122, 124, 126 may have an associated data interface
for communicating data to processing unit 110. The disclosed
embodiments are not limited to any particular data interface
configurations or protocols.
[0139] At step 720, processing unit 110 may analyze the first,
second, and third plurality of images to detect features within the
images, such as lane markings, vehicles, pedestrians, road signs,
highway exit ramps, traffic lights, road hazards, and the like. The
analysis may be performed in a manner similar to the steps
described in connection with FIGS. 5A-5D and 6, above. For
instance, processing unit 110 may perform monocular image analysis
(e.g., via execution of monocular image analysis module 402 and
based on the steps described in connection with FIGS. 5A-5D, above)
on each of the first, second, and third plurality of images.
Alternatively, processing unit 110 may perform stereo image
analysis (e.g., via execution of stereo image analysis module 404
and based on the steps described in connection with FIG. 6, above)
on the first and second plurality of images, the second and third
plurality of images, and/or the first and third plurality of
images. The processed information corresponding to the analysis of
the first, second, and/or third plurality of images may be
combined. In some embodiments, processing unit 110 may perform a
combination of monocular and stereo image analyses. For example,
processing unit 110 may perform monocular image analysis (e.g., via
execution of monocular image analysis module 402) on the first
plurality of images and stereo image analysis (e.g., via execution
of stereo image analysis module 404) on the second and third
plurality of images. The configuration of image capture devices
122, 124, and 126--including their respective locations and fields
of view 202, 204, and 206--may influence the types of analyses
conducted on the first, second, and third plurality of images. The
disclosed embodiments are not limited to a particular configuration
of image capture devices 122, 124, and 126, or the types of
analyses conducted on the first, second, and third plurality of
images.
[0140] In some embodiments, processing unit 110 may perform testing
on system 100 based on the images acquired and analyzed at steps
710 and 720. Such testing may provide an indicator of the overall
performance of system 100 for certain configurations of image
capture devices 122, 124, and 126. For example, processing unit 110
may determine the proportion of "false hits" (e.g., cases where
system 100 incorrectly determined the presence of a vehicle or
pedestrian) and "misses."
[0141] At step 730, processing unit 110 may cause one or more
navigational responses in vehicle 200 based on information derived
from two of the first, second, and third plurality of images.
Selection of two of the first, second, and third plurality of
images may depend on various factors, such as, for example, the
number, types, and sizes of objects detected in each of the
plurality of images. Processing unit 110 may also make the
selection based on image quality and resolution, the effective
field of view reflected in the images, the number of captured
frames, the extent to which one or more objects of interest
actually appear in the frames (e.g., the percentage of frames in
which an object appears, the proportion of the object that appears
in each such frame, etc.), and the like.
[0142] In some embodiments, processing unit 110 may select
information derived from two of the first, second, and third
plurality of images by determining the extent to which information
derived from one image source is consistent with information
derived from other image sources. For example, processing unit 110
may combine the processed information derived from each of image
capture devices 122, 124, and 126 (whether by monocular analysis,
stereo analysis, or any combination of the two) and determine
visual indicators (e.g., lane markings, a detected vehicle and its
location and/or path, a detected traffic light, etc.) that are
consistent across the images captured from each of image capture
devices 122, 124, and 126. Processing unit 110 may also exclude
information that is inconsistent across the captured images (e.g.,
a vehicle changing lanes, a lane model indicating a vehicle that is
too close to vehicle 200, etc.). Thus, processing unit 110 may
select information derived from two of the first, second, and third
plurality of images based on the determinations of consistent and
inconsistent information.
[0143] Navigational responses may include, for example, a turn, a
lane shift, a change in acceleration, and the like. Processing unit
110 may cause the one or more navigational responses based on the
analysis performed at step 720 and the techniques as described
above in connection with FIG. 4. Processing unit 110 may also use
data derived from execution of velocity and acceleration module 406
to cause the one or more navigational responses. In some
embodiments, processing unit 110 may cause the one or more
navigational responses based on a relative position, relative
velocity, and/or relative acceleration between vehicle 200 and an
object detected within any of the first, second, and third
plurality of images. Multiple navigational responses may occur
simultaneously, in sequence, or any combination thereof.
[0144] Traffic Sign Detection System
[0145] In some embodiments, system 100 may include a traffic sign
detection system configured to detect traffic signs, such as road
signs that provide warning or alters to drivers about road or
driving conditions. One or more of image capture devices 122, 124,
and 126 may capture one or more images of an environment in a field
of view ahead of vehicle 200. Processing unit 110 may process the
images using various image processing methods disclosed herein to
identify a traffic sign from the images. Based on the identified
traffic sign, system 100 may provide an alert to the driver of
vehicle 200, such that the driver may take a suitable navigational
action.
[0146] Various methods have been proposed for detecting traffic
signs, such as triangular and/or rhombus traffic signs. One
conventional method detects straight lines in an image using a
Hough transformation that is known in the art and then tries to
find sets of lines that create triangles. Another conventional
method follows similar principles except that it uses points rather
than lines. A generalized Hough transform known in the art has been
proposed to search for triangle shapes in edge images. There are,
however, issues with these approaches. First, the Hough transform
methods require significant memory structures if one wishes to
retain lines with high angular and localization accuracy. Second,
such methods are not conductive to parallel implementations because
they end up with complex serial computation stages. Third, the
running time is data dependent and not predictable which may be a
problem for real-time systems. The present disclosure relates to a
highly parallelizable and deterministic algorithm for detecting
traffic signs. The disclosed algorithm uses a convolution with a
template or a small set of templates followed by thresholding a
non-local maxima suppression (e.g., selecting the highest response
in a local region), which is discussed in detail below.
[0147] As shown in FIG. 8, system 100 may detect a traffic sign 800
appearing in the front field of view of vehicle 200. Traffic sign
800 may be any suitable sign providing driving related information
to the driver. For example, traffic sign 800 may provide the driver
with information regarding the road condition (e.g., alerting the
driver of a blocked road ahead, sharp curve ahead, falling rocks
ahead, etc.), an instruction to slow down or stop, an instruction
to turn on wiper blades and/or headlights, a warning about crossing
animals, students, pedestrians, trains, etc.
[0148] FIG. 9 shows example traffic signs 901-907 that system 100
may detect. Traffic signs 901-907 may also be referred to as
warning signs 901-907. System 100 may detect traffic signs having
any suitable shapes. For example, as shown in FIG. 9, traffic signs
having triangular or rhombus shapes (e.g., diamond shapes) may be
detected. Other shapes, such as pentagon, square, rectangle, etc.,
may also be detected by system 100. In some embodiments, system 100
may detect other signs that are not traffic signs (e.g., warning
signs posted on a wall or building in a work environment that warn
workers about potential hazards).
[0149] As discussed below, warning signs having triangular shapes
are examples of traffic signs that system 100 may detect, and other
signs (including other traffic signs and other warning signs)
having other shapes may also be detected by system 100. For
example, warning signs according to the Vienna Convention are
upward or downward pointing equilateral triangular signs with a red
border, white interior, and typically some black markings (e.g., a
deer as shown in warning sign 905 and a train as shown in warning
sign 907) indicating the content of warning. System 100 may detect
traffic signs from a sequence of images captured by a front facing
driver assistance camera, such as, for example, a camera included
in one or more of image capture device 122, 124, and 126.
[0150] FIG. 10 is an exemplary block diagram of memory 140 and/or
150, which may store instructions for performing one or more
operations for detecting traffic signs consistent with disclosed
embodiments. As shown in FIG. 10, memory 140 may store one or more
modules for performing the traffic sign recognition or detection
described herein. For example, memory 140 may store a traffic sign
detection module 1002 and a warning module 1004. Application
processor 180 and/or image processor 190 may execute the
instructions stored in any of modules 1002 and 1004 included in
memory 140. One of skill in the art will understand that references
in the following discussions to processing unit 110 may refer to
application processor 180 and image processor 190 individually or
collectively. Accordingly, steps of any of the following processes
may be performed by one or more processing devices.
[0151] Traffic sign detection module 1002 may store instructions
which, when executed by processing unit 110, may detect a traffic
sign appearing in the front, side, or rear field of view of vehicle
200, such as one or more of signs 901-907. In some embodiments, a
camera (e.g., a camera included in one or more of image capture
devices 122, 124, and 126) may capture one or more images, or
frames of images, of an environment within the front, side, or rear
field of view, as vehicle 200 travels along a road. Traffic sign
detection module 1002 may process the plurality of images received
from at least one of image capture devices 122-126 to detect a
traffic sign. System 100 may determine that the detected sign is a
traffic sign based on the shape of the sign, and/or based on the
content shown in the sign.
[0152] Warning module 1004 may store instructions which, when
executed by processing unit 110, may provide a warning signal to
the driver about the detected traffic sign. The warning signal may
be a video, audio, image, vibration signal, or a combination
thereof. For example, warning module 1004 may cause speakers 360 of
vehicle 200 to produce an audio alert to the driver, such as, for
example, "train crossing ahead," based on detection of a traffic
sign indicating that train tracks are ahead. As another example,
the warning module 1004 may cause a display of vehicle 200, such as
a center console display configured for displaying navigation
information and other vehicle information, to display an image
associated with the detected traffic sign. The image associated
with the detected traffic sign may be an actual image of the
traffic sign. The image associated with the detected traffic sign
may be another image that closely represent the warning content of
the detected traffic sign (e.g., a cartoon version corresponding to
the actual traffic sign). As another example, warning module 1004
may cause a tactile feedback device (not shown) associated with the
steering wheel or the driver's seat to generate a vibration signal
to alert the driver about the detected traffic sign. The vibration
signal may be customized to correspond to different warning
contents of different traffic signs.
[0153] FIG. 11 shows an exemplary outline of a high level
functional design 1100 of a traffic sign recognition or detection
system that may be part of system 100. That is, FIG. 11 shows an
overall process implemented in the traffic sign detection system to
detect the traffic sign for processing images to detect a traffic
sign. The traffic sign detection system may include various
components discussed above, such as image capture devices 122-126,
processing unit 110, etc., to perform the various functions. As
outlined in the exemplary high level functional design 1100, the
traffic sign detection system may detect single-frame candidates
1110. Candidates may refer to features (such as the triangle shape
of a traffic sign) included in the captured images that may be used
to reconstruct a traffic sign. In some embodiments, in each frame,
new single-frame candidates 1110 may be detected from captured
images of an environment in the front field of view of vehicle 200,
and these may be matched to previously detected multi-frame
candidates 1120, as indicated by box 1130. Multi-frame candidates
1120 may include candidates tracked from previous, multiple frames
and may include information about the traffic sign accumulated
through the previous, multiple frames. The matched frame(s) may be
subject to one or more classification processes, which may include
two sub-classifications: a general type classification 1140, which
may be a gating classifier, followed by an approval classifier 1150
trained on examples of that particular type of traffic sign.
Detected traffic signs (e.g., traffic signs reconstructed from the
captured images), as a result of the approval classifier 1150, may
be used to update the multi-frame candidates 1120. It is understood
that although traffic signs may be detected (e.g., reconstructed
from the captured images) after completing all of the functional
stages included in the exemplary high level functional design 1100,
it is possible that the traffic signs may be detected and output at
any one of stages or steps shown in FIG. 11. For example, depending
on the quality of detection and/or the desired quality of
detection, traffic signs may be detected and output as a result of
detecting the single-frame candidates 1110, or may be detected and
output as a result of the matching process 1130, or may be detected
and output as a result of the general type classification 1140.
[0154] FIG. 12 is a flowchart showing an exemplary process 1200 for
efficiently detecting single-frame candidates 1110. System 100 may
implement process 1200 for processing images to detect single-frame
candidates 1110, which, as shown in FIG. 11, may be further
compared with pre-acquired multi-frame candidates, for detecting a
traffic sign. The single-frame candidates 1110 may be referred to
as attention candidates in below discussions. Although five steps
are shown in FIG. 12, single-frame candidates 1110 may be detected
in one or more of the five steps, in any order. The steps may
include an initial attention step 1210, a first rough classifier
step 1220, a first alignment classifier step 1230, a second rough
classifier step 1240, and a second alignment classifier step 1250.
In the initial attention step 1210, attention candidates may be
detected from an image of an environment captured by one or more
image capture devices 122-126. The first rough classifier step
1220, the first alignment classifier step 1230, the second rough
classifier step 1240, and the second alignment classifier step 1250
are additional steps that system 100 may perform to process the
attention candidates in order to reconstruct a detected traffic
sign. Depending on the desired detection quality and/or the actual
detection quality, a traffic sign may be reconstructed and detected
at any one of the steps 1210-1250. For example, a traffic sign may
be identified or detected based on the attention candidates
detected at step 1210. In some embodiments, processing unit 110 may
reconstruct the traffic sign based on the local maxima identified
from the pixels.
[0155] FIGS. 13A-13E illustrate exemplary results of the image
after it is processed in the processing steps 1210-1250 shown in
FIG. 12. FIG. 13A shows a result of an image after being processed
by the initial attention step 1210. The image after being processed
by the initial attention step 1210 may be subject to the first
rough classifier step 1220. FIG. 13B shows a result of the image
after being processed by the first rough classifier step 1220. The
image after being processed by the first rough classifier step 1220
may be subject to the first alignment classifier step 1230. FIG.
13C shows a result of the image after being processed by the first
alignment classifier step 1230. The image after being processed by
the first alignment classifier step 1230 may be subject to the
second rough classifier step 1240. FIG. 13D shows a result of the
image after being processed by the second rough classifier step
1240. The image after being processed by the second rough
classifier step 1240 may be subject to the second alignment
classifier step 1250. FIG. 13E shows a result of the image after
being processed by the second alignment classifier step 1250.
[0156] An initially large number of features or attention
candidates (including, e.g., a house 1310, trees 1320, an object
1330, and a traffic sign 1340) are gradually reduced, after being
subject to a plurality of processes (e.g., processes shown in FIG.
14), to one aligned candidate (e.g., traffic sign 1340) ready for
classification. The term "attention candidates" refer to features
included in an captured image that draw the attention of a viewer.
The second rough classifier step 1240 and second alignment
classifier step 1250 may be more discriminatory and thus more
expensive to compute than the first rough classifier step 1220 and
first alignment classifier step 1230. In some embodiments, the
initial attention step 1210, the first rough classifier step 1220,
and the first alignment classifier step 1230 may be designed to
have very few or no missed detections at the expense of a large
number of false detections. A balance between the number of missed
detections and the number of false detections may be achieved
through design adjustments. Below discussion focuses on the design
of the initial attention step 1210 such that fast and efficient
attention detection is achieved with no or very few missed
detections.
[0157] FIG. 14 is a flowchart showing an exemplary process 1400 for
detecting a traffic sign. In some embodiments, process 1400 may be
part of the initial attention step 1210. The initial attention
algorithm disclosed herein may use a vector-microcode-processor
efficient template matching technique. In some embodiments of this
technique, a template image of fixed size may be matched to a
plurality of images sampled based on the original images of an
environment containing a traffic sign, which were captured by one
or more of image capture devices 122-126. The disclosed template
matching technique may be efficiently performed by a vector
microcode processor, such as, for example, processing unit 110.
[0158] As compared to other conventional initial attention
algorithms, the disclosed initial attention algorithm may not
require rotation and/or projection of the images. First, one or
more of the image capture devices 122-126 may acquire one or more
images of an environment including a traffic sign (step 1410). For
example, the one or more images may include an environment
appearing in the front field of view of vehicle 200. The image of
the environment may include a traffic sign. For convenience, the
following discussion focuses on processing one image having a
traffic sign, although it is understood that more than one image
may be processed. Processing unit 110 may be programmed to receive
the acquired image from the one or more of the image capture
devices 122-126. Processing unit 110 may transform the image (step
1420). In some embodiments, prior to transforming the image,
processing unit 110 may crop the image to reduce its size such that
only the region most likely having the traffic sign is processed.
This may reduce the computation time, making the process more
efficient.
[0159] Various transformation methods may be used to transform the
image. FIGS. 15A-15B illustrates an exemplary transformation,
referred to as "distance to theta transformation," consistent with
one embodiment of the present disclosure. FIGS. 15A-15B illustrates
the transformation method using a traffic sign having a triangular
shape as an example. The transformation method may also be used to
detect traffic signs having other shapes, such as rhombus shapes,
square shapes, circular shapes, octagon shapes, etc. FIG. 15A shows
an image of a traffic sign at the pixel level. In the example shown
in FIGS. 15A-15B, the triangle is an equilateral triangle. The
sides of the triangle are lines at 60 degrees. A filter that
enhances gradients of 60 degrees may be used. However, such a
transformation may be dependent on the strength of the edges. A
strong vertical edge may give a stronger response than a weak
60-degree edge. Instead, the example transformation method is based
on horizontal and vertical gradients for each pixel of the image.
For each pixel point, a pair of gradients d.sub.x and d.sub.y may
be calculated using any suitable method. For example, in some
embodiments, a non-linear filter, such as a Sobel filter, may be
applied to the image to calculate the gradients at each pixel
point. An angle .theta. may be computed from the gradients:
.theta.=tan.sup.-1 (|dx|/|dy|). A distance between the angle
.theta. and 60 degrees (or .pi./3 radians) may be calculated as:
I.sub.x,y=e-(.pi./3-.theta.).sup.2/.sigma., where .sigma. is a
predetermined number. FIG. 15B shows a relationship between the
distance I.sub.x,y and the angle .theta.. The bell-shaped curve in
FIG. 15B shows distance I.sub.x,y ranges from 0 to 1, as the angle
.theta. ranges from 0 to 90 degrees. The distance I.sub.x,y may be
computed using a look up table. The look up table may include data
entries of d.sub.x, d.sub.y, and I.sub.x,y that are pre-calculated.
When d.sub.x and d.sub.y associated with each pixel point is
calculated by the non-linear filter, such as the Sobel filter,
processing unit 110 may obtain the distance I.sub.x,y by referring
to the look up table with the calculated d.sub.x and d.sub.y.
[0160] FIG. 16 shows an exemplary result of applying the distance
to theta transformation to an image. FIG. 16 shows, at the top, a
grayscale image 1610 of an environment including a traffic sign,
and at the bottom, a resulting image 1620 after the grayscale image
1610 is subject to the distance to theta transformation. As shown
in FIG. 16, the traffic sign (enlarged view shown in image 1630)
may be detected from the transformed image 1620 and/or may be
subject to further processing.
[0161] It is understood that for traffic signs having different
shapes, other predetermined angle values (other than .pi./3
radians) may be used to compare to the angle .theta.. For example,
for rhombus shapes, .pi./4 radians (rather than .pi./3 radians) may
be used for calculating the distance I.sub.x,y.
[0162] Referring back to FIG. 14, the transformed image may be
sampled, by the processing unit 110, at different sampling rates
and/or sizes to generate a plurality of images having different
sizes (step 1430). The plurality of images may be referred to as a
pyramid of images or image pyramid, shown as pyramid 1700 in FIG.
17. The pyramid of images may include a plurality of levels, such
as, for example, level 0 at 640 pixels by 480 pixels, level 1 at
320 pixels by 240 pixels, and level 2 at 160 pixels by 120 pixels.
The pyramid of images may include more or less levels at other
pixel sizes. Each level may include one image or a plurality of
images having different sizes. The different sizes of the pyramid
of images may be selected linearly (i.e., sizes vary linearly), or
may be selected logarithmically (i.e., sizes vary logarithmically).
In some embodiments, all images included in the pyramid of images
may have sizes varying linearly and/or logarithmically. In some
embodiments, all images included in each level (e.g., level 0) may
have sizes varying linearly and/or logarithmically. In some
embodiments, images included in one level (e.g., level 0) may have
sizes varying linearly, and images included in another level (e.g.,
level 1) may have sizes varying logarithmically.
[0163] Referring back to FIG. 14, processing unit 110 may convolve
the plurality of images with a template image 1710 as shown in FIG.
17 (step 1440). In some embodiments, each of the plurality of
images may be convolved with the template image 1710. The template
image 1710 may have a fixed size. That is, the same template image
1710 may be used for convolving with each of the pyramid of images
1700. In some embodiments, more than one template images may be
used for convolving with the pyramid 1700. For example, a first
template image may be used for convolving with images included in
level 0, a second template image may be used for convolving with
images included in level 1, and a third template image may be used
for convolving with images included in level 2. In some
embodiments, more than one template images may be used for
convolving with more than one images included at each level. For
example, level 0 may include three images, and three template
images may be used for convolving with these three images included
in level 0. When more than one template images are used for
convolving with the pyramid of images, the more than one template
images may have different sizes or the same size with different
resolution (e.g., pixels). Template images may be predetermined
using predetermined training image sets. For example, a principal
component analysis known to a person of ordinary skill in the art
may be applied to predetermined training image sets to obtain
template images.
[0164] Referring back to FIG. 14, processing unit 110 may compare
one or more pixel values of the convolved image to a predetermined
threshold (step 1450). For example, when integer numbers from 0 to
255 are used to indicate the pixel values of a grayscale image, the
predetermined threshold may be any desired number selected from 0
to 255, such as, for example, 64, 128, 200, etc. In some
embodiments, each pixel value of each convolved image may be
compared with the predetermined threshold. In some embodiments,
processing unit 110 may apply a non-maximum suppression process
known in the art to the convolved image after the comparison.
Processing unit 110 may select local maxima of pixel values within
local regions of the convolved images as attention candidates (step
1460). In some embodiments, for each convolved image, the local
maxima of pixel values within the local regions may be selected as
attention candidates. The local maxima are greater than the
predetermined threshold. Each of the local regions may have a
predetermined size that is smaller than the size of each convolved
image. For example, each local region may have a size of 5 pixels
by 5 pixels. The non-maximum suppression process may be applied to
each 5-pixel by 5-pixel local region before the local maxima are
selected. The results (e.g., the attention candidates) of the above
discussed initial attention step 1210 may be directly used to
construct or generate a detected traffic sign, if the quality of
the results meet predetermined criteria. Additionally or
alternatively, the results (e.g., the attention candidates) of the
initial attention step 1210 may be further processed by processing
unit 110 to ultimately detect the traffic sign, for example, at a
higher quality. For example, processing unit 110 may perform one or
more further processes, such as one or more of the first rough
classifier step 1220, the first alignment classifier step 1230, the
second rough classifier step 1240, and the second alignment
classifier step 1250, based on the attention candidates detected in
the above discussed initial attention step 1210 to detect the
traffic sign, including its shape and content, at a higher
quality.
[0165] The foregoing description has been presented for purposes of
illustration. It is not exhaustive and is not limited to the
precise forms or embodiments disclosed. Modifications and
adaptations will be apparent to those skilled in the art from
consideration of the specification and practice of the disclosed
embodiments. Additionally, although aspects of the disclosed
embodiments are described as being stored in memory, one skilled in
the art will appreciate that these aspects can also be stored on
other types of computer readable media, such as secondary storage
devices, for example, hard disks or CD ROM, or other forms of RAM
or ROM, USB media, DVD, Blu-ray, or other optical drive media.
[0166] Computer programs based on the written description and
disclosed methods are within the skill of an experienced developer.
The various programs or program modules can be created using any of
the techniques known to one skilled in the art or can be designed
in connection with existing software. For example, program sections
or program modules can be designed in or by means of .Net
Framework, .Net Compact Framework (and related languages, such as
Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX
combinations, XML, or HTML with included Java applets.
[0167] Moreover, while illustrative embodiments have been described
herein, the scope of any and all embodiments having equivalent
elements, modifications, omissions, combinations (e.g., of aspects
across various embodiments), adaptations and/or alterations as
would be appreciated by those skilled in the art based on the
present disclosure. The limitations in the claims are to be
interpreted broadly based on the language employed in the claims
and not limited to examples described in the present specification
or during the prosecution of the application. The examples are to
be construed as non-exclusive. Furthermore, the steps of the
disclosed methods may be modified in any manner, including by
reordering steps and/or inserting or deleting steps. It is
intended, therefore, that the specification and examples be
considered as illustrative only, with a true scope and spirit being
indicated by the following claims and their full scope of
equivalents.
* * * * *