U.S. patent application number 15/225661 was filed with the patent office on 2016-11-24 for system and method for object recognition and tracking in a video stream.
The applicant listed for this patent is eyeSight Mobile Technologies Ltd.. Invention is credited to Dudi Cohen, Nadav Israel, Itay Katz, Amnon Shenfeld.
Application Number | 20160343145 15/225661 |
Document ID | / |
Family ID | 42104703 |
Filed Date | 2016-11-24 |
United States Patent
Application |
20160343145 |
Kind Code |
A1 |
Israel; Nadav ; et
al. |
November 24, 2016 |
SYSTEM AND METHOD FOR OBJECT RECOGNITION AND TRACKING IN A VIDEO
STREAM
Abstract
The invention provides a system method for object detection and
tracking in a video stream. Frames of the video stream are divided
into regions of interest and a probability is calculated for each
region of interest that the region contains at least a portion of
an object to be tracked. The regions of interest in each frame are
then classified based on the calculated probabilities. A region of
interest (RI) frame is then constructed for each video frame that
reports the classification of regions of interest in the video
frame. Two or more RI frames are then compared in order to
determine a motion of the object. The invention also provides a
system executing the method of the invention, as well as a device
comprising the system. The device may be for example, a portable
computer, a mobile telephone, or an entertainment device.
Inventors: |
Israel; Nadav; (Alfei
Menashe, IL) ; Katz; Itay; (Tel Aviv, IL) ;
Cohen; Dudi; (Beer Sheva, IL) ; Shenfeld; Amnon;
(Tel Aviv, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
eyeSight Mobile Technologies Ltd. |
Herzliya |
|
IL |
|
|
Family ID: |
42104703 |
Appl. No.: |
15/225661 |
Filed: |
August 1, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13147472 |
Aug 2, 2011 |
9405970 |
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PCT/IL2010/000092 |
Feb 2, 2010 |
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15225661 |
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61202157 |
Feb 2, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/00355 20130101;
G06F 3/017 20130101; G06K 9/3241 20130101; G06T 7/11 20170101; G06T
7/215 20170101; G06T 2207/10016 20130101 |
International
Class: |
G06T 7/20 20060101
G06T007/20; G06F 3/01 20060101 G06F003/01; G06K 9/00 20060101
G06K009/00; G06T 7/00 20060101 G06T007/00; G06K 9/32 20060101
G06K009/32 |
Claims
1.-30. (canceled)
31. A system comprising: a processor configured to: partition a
first video frame in a video stream into at least a first region;
classify the first region based on an analysis of one or more
pixels within the first region, wherein a classification of the
first region reflects a probability of a presence of a tracked
object within the first region; compare the classification of the
first region with a classification of another region of another
video frame in the video stream; and based on the comparison of the
classification of the first region and the classification of
another region of another video frame in the video stream,
determine a motion of the tracked object.
32. The system of claim 31, wherein to partition the first video
frame the processor is further configured to partition the first
video frame based on an axis of expected motion of the tracked
object.
33. The system of claim 31, wherein to partition the first video
frame the processor is further configured to: analyze one or more
video frames in the video stream; determine, based on an analysis
of the one or more video frames, one or more high variance regions
within the one or more video frames; and omit the one or more high
variance regions from the first region.
34. The system of claim 31, wherein to classify the first region
the processor is further configured to classify the first region
based on an analysis of one or more regions of one or more other
video frames that precede the first video frame in the video
stream.
35. The system of claim 31, wherein to classify the first region
the processor is further configured to classify the first region
based on a distance between a histogram of the tracked object and a
histogram of the first region.
36. The system of claim 31, wherein to determine a motion of the
tracked object the processor is further configured to detect a
motion pattern of the tracked object based on the classification of
the first region and one or more classifications of one or more
other regions of one or more other video frames in the video
stream.
37. The system of claim 31, wherein to determine a motion of the
tracked object the processor is further configured to: apply a
pattern recognition test to the first region and one or more other
regions of in the video stream; and compute a probability that a
motion pattern associated with the pattern recognition test
occurred during a time window that includes the first video frame
and the one or more other video frames.
38. The system of claim 31, wherein to determine a motion of the
tracked object the processor is further configured to detect a
motion pattern of the tracked object based on the classification of
the first region and one or more inputs from an operating
system.
39. The system of claim 31, wherein to determine a motion of the
tracked object the processor is further configured to detect a
motion pattern of the tracked object based on the classification of
the first region and one or more inputs from an application.
40. The system of claim 31, wherein the processor is further
configured to execute a command associated with the determined
motion of the tracked object.
41. A method comprising: partitioning a first video frame in a
video stream into at least a first region; classifying the first
region based on an analysis of one or more pixels within the first
region, wherein a classification of the first region reflects a
probability of a presence of a tracked object within the first
region; comparing the classification of the first region with a
classification of another region of another video frame in the
video stream; and based on the comparison of the classification of
the first region and the classification of another region of
another video frame in the video stream, determining, by a
processor, a motion of the tracked object.
42. The method of claim 41, wherein partitioning the first video
frame further comprises partitioning the first video frame based on
an axis of expected motion of the tracked object.
43. The method of claim 41, wherein partitioning the first video
frame further comprises: analyzing one or more video frames in the
video stream; determining, based on an analysis of the one or more
video frames, one or more high variance regions within the one or
more video frames; and omitting the one or more high variance
regions from the first region.
44. The method of claim 41, wherein classifying the first region
further comprises classifying the first region based on an analysis
of one or more regions of one or more other video frames that
precede the first video frame in the video stream.
45. The method of claim 41, wherein classifying the first region
further comprises classifying the first region based on a distance
between a histogram of the tracked object and a histogram of the
first region.
46. The method of claim 41, wherein determining a motion of the
tracked object further comprises detecting a motion pattern of the
tracked object based on the classification of the first region and
one or more classifications of one or more other regions of one or
more other video frames in the video stream.
47. The method of claim 41, wherein determining a motion of the
tracked object further comprises: applying a pattern recognition
test to the first region and one or more other regions of in the
video stream; and computing a probability that a motion pattern
associated with the pattern recognition test occurred during a time
window that includes the first video frame and the one or more
other video frames.
48. The method of claim 41, wherein determining a motion of the
tracked object further comprises detecting a motion pattern of the
tracked object based on the classification of the first region and
one or more inputs from an operating system.
49. The method of claim 41, wherein determining a motion of the
tracked object further comprises detecting a motion pattern of the
tracked object based on the classification of the first region and
one or more inputs from an application.
50. A non-transitory computer readable medium having instructions
encoded thereon that, when executed by a processing device, cause
the processing device to: partition a first video frame in a video
stream into at least a first region; classify the first region
based on an analysis of one or more pixels within the first region,
wherein a classification of the first region reflects a probability
of a presence of a tracked object within the first region; compare
the classification of the first region with a classification of
another region of another video frame in the video stream; and
based on the comparison of the classification of the first region
and the classification of another region of another video frame in
the video stream, determine, by the processing device, a motion of
the tracked object.
Description
FIELD OF THE INVENTION
[0001] This invention relates to methods and systems for object
detection and tracking, and to devices containing such systems.
BACKGROUND OF THE INVENTION
[0002] The following prior art publications are considered relevant
for an understanding of the invention:
[0003] Digital Image Processing by Rafael C. Gonzalez, Richard E.
Woods and Steven L. Eddins, Prentice Hall (2004), 10.4.2--Region
Growing.
[0004] E. Deja, M. M. Deja, Dictionary of Distances, Elsevier
(2006).
[0005] Mahalanobis, P C (1936). "On the generalised distance in
statistics". Proceedings of the National Institute of Sciences of
India 2 (1): 49-55).
[0006] Itakura F., "Line spectrum representation of linear
predictive coefficients of speech signals," J. Acoust.Soc. Am., 57,
537(A), 1975.
[0007] James M. Abello, Panos M. Pardalos, and Mauricio G. C.
Resende (editors) (2002). Handbook of Massive Data Sets.
Springer.
[0008] E. R. Berlekamp, Algebraic Coding Theory, McGraw-Hill
1968.
[0009] Richard W. Hamming. Error Detecting and Error Correcting
Codes, Bell System Technical Journal 26(2):147-160, 1950.
[0010] Dan Gusfield. Algorithms on strings, trees, and sequences:
computer science and computational biology. Cambridge University
Press, New York, N.Y., USA, 1997).
[0011] U.S. Pat. Nos. 5,767,842 and 6,650,318
[0012] Entering data into a data processing device is accomplished
using a data input device such as a keyboard, mouse, or joystick.
Although electronic devices are constantly being miniaturized, the
size of the various associated data input devices cannot be
substantially decreased since they must conform to the size of a
user's hands. Methods for inputting data have therefore been
devised in which the user's hands do not have to touch the device.
U.S. Pat. No. 5,767,842 to Korth, and U.S. Pat. No. 6,650,318 to
Amon for example, disclose an optical system in which a camera is
used to monitor a user's hand and finger motions. A software
application interprets these motions as operations on a physically
non-existent computer keyboard or other input device. In these
systems, the camera has a fixed position, so that the background of
the images remains constant. This allows the software application
to make use of information present in the constant background in
order to detect the user's hands in each image. This system,
therefore, cannot be used in a device that in use is moved because,
in this case, the background of the images is not constant, so
there is no reliable background information in the images. Devices
that are moved in use include hand-held devices such as a personal
digital assistant (PDA), a mobile telephone, a digital camera, and
a mobile game machine.
SUMMARY OF THE INVENTION
[0013] In its first aspect, the present invention provides a system
for object detection and tracking in a video stream. The system of
the invention is based on two separate logical hierarchies. The
first hierarchy partitions the video stream into regions of
interest which act as standalone motion sensors in the environment,
independently responsible for calculating the likeliness of the
tracked object being present in the region. The second hierarchy
monitors the behavior of the set of regions over time and, based on
patterns of likeliness, calculates the position and motion
parameters of the tracked object.
[0014] The system of the invention comprises a memory storing
frames of a video stream to be analyzed by the system. A processor
fetches frames of the video stream stored in the memory. An object
detection module classifies regions of interest in each frame
according to the probability that the region of interest contains
at least a portion of a predetermined object to be tracked. As
explained below, object detection by the object detection module
does not involve edge detection of the objection in the frames. An
object tracking module receives as its input the classified frames
output by the object detection module and, by comparing consecutive
classified frames, determines the motion of the object. The system
of the invention may be used to input operating system (OS)
commands to the device instead of, or in addition to, any input
devices associated with the device such as a keyboard, mouse or
joystick. The system of the invention my be used in any type of
data processing device such as a personal computer (PC), a portable
computer such as a PDA, a laptop or a palm plot, a mobile
telephone, a radio or other entertainment device, a vehicle, a
digital camera, a mobile game machine, a computerized medical
device and a smart house product.
[0015] Depending on the application, the processor may optionally
include a pattern to recognition module that identifies patterns of
motion of the tracked object from among a predetermined set of
object motions. The system may further comprise an OS command
execution module that stores a look-up table that provides, for
each of one or more of the predetermined motion patterns, an
associated OS command. When one of the predetermined object motions
is identified, the OS command associated with the motion is
executed by the system.
[0016] In its second aspect, the invention provides a data
processing device comprising the system of the invention. The data
processing device may be, for example, a personal computer (PC), a
portable computer such as a PDA, a laptop, or a mobile telephone, a
radio or other entertainment device, a vehicle, a digital camera or
a mobile game machine. The device of the invention has a video
camera and processor configured to carry out object detection and
object tracking, as explained above. The object to be detected and
tracked may be for example a hand or finger of a user or a hand
held stylus or other predefined or specific device.
[0017] The device of the invention comprises a memory that stores a
look-up table that provides, for each recognized motion an
associated OS command. When a motion pattern is detected by the
pattern identification module, the OS command associated with the
motion is looked up in the look-up and the OS command associated
with the motion is then executed. The OS command may be, for
example, activate functions such as Speaker On/Off, Next/Previous
track in the MP3/IPTV, control map views in the GPS application and
to switch on voicemail service,
[0018] In accordance with this aspect of the invention, the frames
of the video stream are partitioned into two or more regions of
interest. For each region of interest, a statistical analysis of
the pixels in the region of interest is performed. For example, the
statistical analysis may comprise generating a histogram for each
of one or more functions defined on the pixels of the region of
interest. The function may be, for example, an intensity of any one
of the colors red, green, or blue of the pixels, or any one of the
hue, saturation or luminance of the pixels. The histograms may be
histograms of a single variable or may be multivariable histograms,
in which the frequency of n-tuples of pixel properties is tallied.
The statistical analysis may also comprise calculating values of
statistical parameters such as an average, mode, standard
deviation, or variance of any one or more of the histograms. The
results of the statistical analysis of region of interest are used
to classify the region according to the probability that the region
contains at least a portion of the object being detected. For each
frame analyzed, a "region of interest (RI) frame" is generated
which is a representation of the classifications of the regions of
interest of the frame.
[0019] One or more pattern detection modules are used to detect
specific motion patterns of the object from the RI frames. Each
pattern detection module outputs a probability that the specific
motion pattern detected by the pattern detection module occurred
during the time window. The outputs of the one or more pattern
recognition modules are input to a motion recognition module that
determines a motion pattern most likely to have occurred. The
determination of the motion detection module is based upon the
probabilities input from the pattern recognition modules and may
also take into account an external input, for example, an input
from the operating system or the application being run.
[0020] Thus, in its first aspect, the invention provides a system
for object detection and tracking in a video stream, comprising:
[0021] (a) a processor comprising an object detection module and an
object tracking module;
[0022] wherein the object detection module is configured to: [0023]
(i) calculate, for each of one or more regions of interest in each
of two or more frames in the video stream, a probability that the
region of interest contains at least a portion of an object to be
tracked; and [0024] (ii) classify the regions of interest in each
of the two or more frames according to the calculated probabilities
and generate a region of interest (RI) frame for each video frame,
the RI frame reporting the classification of regions of
interest;
[0025] and wherein the object tracking module is configured to:
[0026] (i) compare two RI frames generated by the object detection
module and determine a motion of the object.
[0027] The object tracking module may comprise one or more pattern
detection modules, each pattern detection module being configured
to calculate a probability that a specific pattern of motion of the
tracked object during a time window occurred during the time
window. The object tracking module may further comprise a motion
recognition module determining a motion pattern most likely to have
occurred based upon the probabilities generated by the one or more
pattern detection modules. The determination of the motion
recognition module may involve taking into account an external
signal.
[0028] The system of the invention may further comprise an
operating system (OS) command execution module configured to
execute an OS command associated with an identified pattern of
motion.
[0029] In its second aspect, the invention provides a method for
object detection and tracking in a video stream, comprising: [0030]
calculating, for each of one or more regions of interest in each of
two or more frames in the video stream, a probability that the
region of interest contains at least a portion of an object to be
tracked; [0031] (ii) classifying the regions of interest in each of
the two or more frames according to the calculated probability and
generating a region of interest (RI) frame for each video frame,
the RI frame reporting the classification of regions of interest;
and [0032] (i) comparing two or more RI frames generated by the
object detection module and determine a motion of the object.
[0033] The probability that a region of interest contains at least
a portion of the object to be tracked may be obtained in a method
comprising: [0034] (a) for each of one or more regions of interest
in each frame in the video stream, calculating a statistical
analysis of the pixels in the region of interest; [0035] (b)
calculating a discrete classification of the region of interest in
a calculation involving the statistical analysis of the region of
interest in one or more previous frames of the video stream.
[0036] The statistical analysis may comprise generating a histogram
for each of one or more functions defined on pixels of the region
of interest. One or more of the functions may be selected from the
group comprising: [0037] (a) an intensity of any one of the colors
red, green, or blue of the pixels; and [0038] (b) any one of a hue,
saturation or luminance of the pixels.
[0039] The method of the invention may further compris calculating
values of statistical parameters of one or more of the functions.
One or more of the statistical parameters may be selected from the
group comprising: [0040] (a) an average; [0041] (b) a mode; [0042]
(c) a standard deviation; and [0043] (d) a variance.
[0044] The step of comparing two or more RI frames may comprise:
[0045] (a) for each frame, and for each of the classified regions
of interest in the frame, comparing the classification of the
region of interest with the classification of the region of
interest in a plurality of frames obtained in a time window
containing the frame; [0046] (b) determining, on the basis of the
comparison, whether or not the selected region of interest contains
the object to be tracked; [0047] (c) reclassifying, on the basis of
this determination, the region of interest, according to whether or
not the region of interest contains the object to be tracked; and
[0048] (d) calculating one or more tracking parameters of the
object's motion based upon changes in the states of two or more of
the regions during a time window.
[0049] The tracking parameters may be selected from the group
comprising: [0050] (a) direction of movement of the object; [0051]
(b) a speed of movement of the object; [0052] (c) an acceleration
of the object; [0053] (d) a width of the object in pixels; and
[0054] (e) a height of the object in pixels; and [0055] (f)
location of the object in the frame
[0056] In another of its aspects, the invention provides a data
processing device comprising a system of the invention. The data
processing device may be selected from the group comprising: [0057]
(a) a personal computer (PC); [0058] (b) a portable computer such
as a PDA or a laptop. [0059] (c) a mobile telephone; [0060] (d) a
radio; [0061] (e) an entertainment device; [0062] (f) Smart Home;
[0063] (g) a vehicle; [0064] (h) a digital camera [0065] (i)
kitchen appliance; [0066] (j) an media player or media system,
[0067] (k) location based devices; and [0068] (l) a mobile game
machine. [0069] (m) a pico projector or an embedded projector
[0070] (n) medical display device. [0071] (o) an in-car/in-air
Infotainment system.
[0072] The device of the invention may further comprise one or both
of a video camera and a display screen.
[0073] One or more of the patterns of motion of the tracked object
may be selected from the group comprising: [0074] (a) a width of
the object in pixels increased during the time window; [0075] (b)
the width of the object in pixels decreased during the time window;
[0076] (c) the object moved closer to the camera; [0077] (d) the
object moved away from the camera; [0078] (e) the object moved in a
predetermined path; [0079] (f) the object rotated; [0080] (g) the
object was stationary; [0081] (h) the object performed any type of
motion; [0082] (i) the object performed a flicking motion; [0083]
(j) the object accelerated; [0084] (k) the object decelerated; and
the object moved and then stopped.
[0085] The processor may further comprise an operating system (OS)
command execution module configured to execute an OS command of the
device associated with an identified pattern of motion. The OS
commands may be selected from the group comprising: [0086] (a)
depressing a virtual key displayed on a display screen of the
device; [0087] (b) moving a curser appearing on a display screen of
the device to a new location on the screen; [0088] (c) rotating a
selection carousel; [0089] (d) switching between desktops; [0090]
(e) running on the central processor unit a predetermined software
application; [0091] (f) turning off an application; [0092] (g)
turning the speakers on or off; [0093] (h) turning volume up/down;
[0094] (i) skipping to the next or previous track in a media player
or between IPTV channels; [0095] (j) controlling a GPS application;
[0096] (k) switching on voicemail service; [0097] (l) navigating in
photo/music album gallery; [0098] (m) scrolling web-pages, emails,
documents or maps; [0099] (n) controlling actions in mobile games;
and [0100] (o) controlling interactive video or animated
content.
[0101] It will also be understood that the system according to the
invention may be a suitably programmed computer. Likewise, the
invention contemplates a computer program being readable by a
computer for executing the method of the invention. The invention
further contemplates a machine-readable memory tangibly embodying a
program of instructions executable by the machine for executing the
method of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0102] In order to understand the invention and to see how it may
be carried out in practice, embodiments will now be described, by
way of non-limiting example only, with reference to the
accompanying drawings, in which:
[0103] FIG. 1 shows schematically a video stream comprising a
plurality of frames to partitioned into regions of interest;
[0104] FIG. 2 shows a system for object detection and tracking in
accordance with one embodiment of the invention;
[0105] FIG. 3 shows a method of object detection in accordance with
one embodiment of the invention;
[0106] FIG. 4a shows three frames in a video stream and FIG. 4b
shows region of interest (RI) frames obtained from the frames of
FIG. 4a.
[0107] FIG. 5 shows a method for object tracking in accordance with
one embodiment of the invention;
[0108] FIG. 6 shows a data processing device incorporating the
system of the invention for object detection and tracking;
[0109] FIG. 7 shows examples of motion patterns and their use in
executing OS commands in various types of devices; and
DETAILED DESCRIPTION OF EMBODIMENTS
[0110] FIG. 1 shows schematically a video sequence 2 comprising a
sequence of video frames 4. Four frames, 4a, 4b, 4c, and 4d are
shown in FIG. 1. This is by way of example only, and the video
sequence 2 can contain any number of video frames that is at least
2. Each frame consists of a plurality of pixels which are
partitioned into regions of interest 6, the boundaries of which are
indicated in FIG. 1 by broken lines 8. The frames 4 are shown in
FIG. 1 divided into 36 regions of interest 6 (six rows of six
regions of interest). This is by way of example only, and the
frames 4 can be divided into any number of regions of interest that
is at least two. The regions of interest may have any shape, and
may overlap.
[0111] FIG. 2 shows a system 40 for object detection and tracking
in a video stream, such as the video stream 2, in accordance with
one embodiment of the invention. The video stream 2 is input into a
memory 44. The memory 44 is accessible by a processor 46 which
fetches the frames 6 of the video stream 2 stored in the memory 44.
The processor 46 comprises an object detection module 45 which
analyzes the video stream 2, and an object tracking module 47 which
analyzes the output of the object detection module 45, as explained
below. The object detection module 45 classifies regions of
interest in each frame according to the probability that the region
of interest contains at least a portion of the object to be
tracked. The object tracking module 47 receives as its input the
classified frames output by the object detection module 45, and, by
comparing the classifications of the regions of interest in
consecutive frames, determines the motion of the object.
[0112] The system 40 may further comprise an OS command execution
module 51. In this case, the memory 44 stores a look-up table that
provides, for each of one or more of the predetermined motion
patterns, an associated OS command. When one of the predetermined
object motions is identified, the OS command associated with the
motion is executed.
[0113] A user input device 48 may be used to input any relevant
data into the system 40, such as an identification of the video
stream 2, or the parameters to be analyzed by the processor 46, as
explained below. The video stream 2, as well as the results of the
processing can be displayed on a display device 50, such as a CRT
screen, LCD, or printer.
[0114] FIG. 3 shows a process 20 for object detection in a frame of
the video stream 2 that is carried out by the object detection
module 45 of the processor 46 in accordance with one embodiment of
the invention. The process begins with step18 in which the frame is
retrieved from the memory 44 and is partitioned into two or more
regions of interest 6. The regions of interests can either be
created dynamically by using any object segmentation technique
known in the art, such as seeded region growing as disclosed in
Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins, Digital
Image Processing, Prentice Hall (2004), Section 10.4.2 (Region
Growing). Alternatively, the regions of interest may be statically
defined as a group of regions such by a fixed 6 by 6 matrix
segmenting the frame into 36 regions. Then, in step 24 a region of
interest 6 is selected in the frame, and a statistical analysis of
the pixels in the region of interest is performed in step 26. For
example, the statistical analysis may comprise generating a
histogram 10 for each of one or more functions defined on the
pixels of the region. The function may be, for example, an
intensity of any one of the colors red, green, or blue of the
pixels, or any one of the hue, saturation or luminance of the
pixels. The histograms may be histograms of a single variable or
may be multivariable histograms, in which the frequency of n-tuples
of pixel properties is tallied. The statistical analysis may also
comprise calculating values of statistical parameters such as an
average, mode, standard deviation, or variance of any one or more
of the histograms. The results of the statistical analysis are
stored in the memory 44.
[0115] In step 30, the region of interest that was just analyzed is
classified. The classification of a region of interest is a
discrete function that describes the probability of the presence of
the tracked object in the region of interest. The classification of
the region of interest is determined in a method involving the
statistical analysis of the region of interest in the current frame
and the statistical analysis of the region of interest in one or
more previous frames of the video stream. In one embodiment, a
distance function is applied to calculate the similarity of various
parameters and statistical features in the selected region to
parameters and statistical features presenting a tracked object
passing in the region. Region and object parameters may include for
example the existence of different shapes and contours and their
frequencies, while statistical features may include for example the
histograms of hue, luminance and saturation and the color pattern.
The combined distance result is compared to the results of the
region of interest in previous frames. For example, a distance in
hue parameters may indicate that an object of the same color as the
tracked object has entered the region. This may cause the region to
be classified with higher probability of containing the tracked
object. The distance function may be, for example, a Euclidean
Distance (E. Deja, M. M. Deja, Dictionary of Distances, Elsevier
(2006)), a Mahalanobis Distance (Mahalanobis, P C (1936). "On the
generalised distance in statistics". Proceedings of the National
Institute of Sciences of India 2 (1): 49-55) a Itakura saito
Distance (Itakura F., "Line spectrum representation of linear
predictive coefficients of speech signals," J. Acoust.Soc. Am., 57,
537(A), 1975), a Chebyshev Distance (James M. Abello, Panos M.
Pardalos, and Mauricio G. C. Resende (editors) (2002). Handbook of
Massive Data Sets. Springer.), a Lee Distance (E.R. Berlekamp,
Algebraic Coding Theory, McGraw-Hill 1968), a Hamming Distance
(Richard W. Hamming. Error Detecting and Error Correcting Codes,
Bell System Technical Journal 26(2):147-160, 1950), or a
Levenshtein Distance (Dan Gusfield. Algorithms on strings, trees,
and sequences: computer science and computational biology.
Cambridge University Press, New York, N.Y., USA, 1997). The
classification of the selected region of interest is stored in the
memory 44 (step 31).
[0116] In step 32 it is determined whether another region of
interest of the frame is to be analyzed by the detection module 45.
If yes, then the process returns to step 24 with the selection of
another region of interest in the current frame. Otherwise, the
process continues with step 34 where a "region of interest (RI)
frame" is generated for the video frame, and the process
terminates. The RI frame of the input video frame is a
representation of the classifications of the regions of interest of
the frame. FIG. 4a shows three frames 110a, b, and c of a video
stream obtained at three different times (times t.sub.0, t.sub.1,
and t.sub.2, respectively). In this example, the frames are
arbitrarily divided into static regions of interest indicated by
broken lines 112. Alternatively, a method for dynamic partition of
the frame into regions of interest can be used. For example,
growing a set of preset seed regions into larger areas which
maintain statistical homogeneity. For example, a seed region
located in the sky area of FIG. 4 will grow until the region meets
the ground where the hue histogram and edge frequency change
significantly. This partitioning process may be assisted by
pre-knowledge of the axis of the expected motion to be tracked or a
statistical analysis of several frames which determines high
variance regions which should be omitted from the original interest
group. An object 114 to be tracked has moved in the frames during
the time interval from t.sub.0 to t.sub.2. FIG. 4b shows region of
interest frames 116a, 116b, and 116c, corresponding to the video
frames 110a, 110b, and 110c, respectively. In the example of FIG.
4, each region of interest was classified into one of two
classifications depending on whether at least a portion of the
object 114 is located in the region of interest (indicated in FIG.
4b by cross hatching of the region of interest), or whether the
region of interest does not contain at least a portion of the
object 114 (unhatched regions of interest in FIG. 4b). Thus, in the
RI frame 116a obtained at time t.sub.0, a region 117a, located on
the right side of the frame has been classified as containing the
object 114. This classification can be obtained by calculating the
similarity rating of the region and the tracked object, for example
by measuring the Euclidian Distance between the hue histogram of
the tracked object and the histogram of the selected region. Using
the same method, in the intermediate frame 116b, two regions 117b
and 117c have been classified as containing the object 114 and in
the later frame 116c, two regions 117d and 117e have been
classified as containing the object 114.
[0117] The tracking module 47 receives as its input the RI frames
generated by the detection module 45 during a time window of the
video stream. The tracking module 47 may operate simultaneously
with the detection module 45, receiving classified frames as they
are generated by the detection module 45. Alternatively, the
tracking module 47 may operate sequentially with the detection
module 45, receiving the classified frames only after all of the
frames of the video stream have been classified.
[0118] FIG. 5 shows an object tracking process carried out by the
object tracking module 47 in accordance with one embodiment of the
invention. In step 52 the RI frames of the time window are input to
the tracking module 47, and in step 54, the RI frames are filtered
for removal of random noise. The filtered RI frames are then input
to one or more independent pattern detecting modules 56. Each
pattern detection module 56 is configured to detect a specific
motion pattern of the object from the filtered RI frames, and
outputs a probability that the specific motion pattern of the
pattern detection module occurred during the time window. Each
pattern detection module 56 applies pattern recognition tests to
some or all of the input RI frames. For example, referring again to
FIG. 4b, the pattern detection module 56 would detect a motion of
the object from the left side of the frame to the right side of the
frame. The output of the one or more pattern recognition modules 56
is input to a motion recognition module 58. The motion recognition
module 58 determines a motion pattern most likely to have occurred
during the time window. The determination of the motion detection
module 58 is based upon the probabilities input from one or more
pattern recognition modules 56 and may also take into account an
external input, for example, an input from the operating system or
the application being run. The motion determination of the motion
recognition module 58 is then output (step 60) and the process
terminates.
[0119] FIG. 6 shows a data processing device 72 comprising the
system 40, in accordance with one embodiment of this aspect of the
invention. The data processing device 72 may be, for example, a
personal computer (PC), a portable computer such as a PDA, a laptop
or a palm plot, or a mobile telephone, a radio or other
entertainment device, a vehicle, a digital camera or a mobile game
machine. The device 72 has a video camera 76. The device 72 may
also be provided with a display screen 74 and various data input
devices such as a keypad 78 having a plurality of keys 80 for
inputting data into the data input device 72.
[0120] The camera 76 views a conical or pyramidal volume of space
86 indicated by broken lines. The camera 76 may have a fixed
position on the device 72, in which case the viewing space 86 is
fixed relative to the device 72, or may be positionable on the
device 72, in which case the viewing space 86 is selectable
relative to the device 72. Images captured by the camera 76 are
digitized by the camera 76 and input to the processor 46 (see also
FIG. 2). The object detection module 45 of the processor 46 detects
a predetermined object 94 in frames obtained by the camera 76, as
explained above. The object 94 may be for example a finger or
entire hand of a user, in various positions, such as an open hand,
closed hand or back hand. The user may use his other hand 89 to
hold the device 2 in use, if the device 2 is a hand-held device.
The hand 89 may also be used to activate real input devices
associated with the device 72, such as activating keys 80 on the
keypad 78.
[0121] The memory 44 stores a look-up table that provides, for each
test an associated OS command. When a motion pattern is detected by
the pattern identification module 49, the OS command associated
with the motion is looked up in the look-up table stored in the
memory 44, and then the OS command associated with the motion is
executed by the OS execution module 51. The OS command may be, for
example, depressing a virtual key displayed on the display screen,
moving a curser appearing on the display screen to a new location
on the screen, running on the processor 46 a software application
stored in the memory 44, or turning off the device 72. The device
may provide an indication that the OS command was executed. For
example, an OS command equivalent to depressing a key on the
virtual keyboard may be indicated by briefly showing the key
depressed on a virtual keyboard on the screen 4, or by briefly
changing the appearance of the key. Other possibilities for
indicating that the OS command was executed include briefly
enlarging or otherwise changing the appearance of a depressed key
or of the cursor on the screen 4, displaying an icon on the screen
4, producing a sound, and vibrating the device.
[0122] FIG. 7 shows examples of motion patterns that may be
recognized by the pattern detection modules 56, and how the motion
patterns may be used to execute an OS command, depending on the
type of device 72. Motion 100 consists of moving a hand towards the
device 72. Motion 102 consists of moving a hand toward the device
72 and then moving the hand away from the device. Motion 104
consists of moving a hand from left to right over the device, and
motion 106 consists of moving a hand from right to left over the
device.
* * * * *