Source: http://www.google.nl/patents/US8730396
Timestamp: 2018-01-16 19:08:17
Document Index: 176572093

Matched Legal Cases: ['art 2003', 'art 2008', 'art 2008', 'art 2012', 'art 2009', 'art 2002', 'art 2001', 'art 2002', 'art 2002', 'art 2003', 'art 2003', 'art 2005', 'art 2005', 'art 2004', 'art 2006', 'art 2006', 'art 2006', 'art 2006', 'art 2006', 'art 2007', 'art 2007', 'art 2006', 'art 2009', 'art 2010', 'art 2010', 'art 2009', 'art 2009', 'art 2011']

Patent US8730396 - Capturing events of interest by spatio-temporal video analysis - Google Patenten
A computer implemented method and system for capturing events of interest by performing a spatio-temporal analysis of a video are provided. A continuous video stream containing a series of image frames is acquired over time. Each of the image frames is represented by horizontal spatial coordinates and...http://www.google.nl/patents/US8730396?utm_source=gb-gplus-sharePatent US8730396 - Capturing events of interest by spatio-temporal video analysis
Publicatienummer US8730396 B2
Aanvraagnummer US 12/876,166
Publicatiedatum 20 mei 2014
Aanvraagdatum 6 sept 2010
Prioriteitsdatum 23 juni 2010
Ook gepubliceerd als US20110317009
Publicatienummer 12876166, 876166, US 8730396 B2, US 8730396B2, US-B2-8730396, US8730396 B2, US8730396B2
Uitvinders Suresh Kirthi Kumaraswamy, Puneeth Bilagunda Chandrashekar, Hemanth Kumar Sangappa, Venu Belagur Krishnamurthy
Oorspronkelijke patenteigenaar MindTree Limited
Patentcitaties (132), Niet-patentcitaties (4), Verwijzingen naar dit patent (3), Classificaties (5), Juridische gebeurtenissen (2)
Capturing events of interest by spatio-temporal video analysis
US 8730396 B2
A computer implemented method and system for capturing events of interest by performing a spatio-temporal analysis of a video are provided. A continuous video stream containing a series of image frames is acquired over time. Each of the image frames is represented by horizontal spatial coordinates and vertical spatial coordinates of a two dimensional plane. A temporal dimension is assigned across the image frames of the video stream. A spatio-temporal analysis image is constructed based on a user-defined line of analysis on each of one or more of the image frames. The spatio-temporal analysis image is constructed by concatenating a series of temporally-successive linear pixel arrays along the temporal dimension. Each of the linear pixel arrays comprises an array of pixels along the line of analysis defined on each of one or more of the image frames. The constructed spatio-temporal analysis image is segmented for capturing the events of interest.
1. A computer implemented method for capturing events of interest by performing a spatio-temporal analysis of a video, comprising:
acquiring a continuous video stream containing a series of image frames over time, wherein each of said image frames is represented by horizontal spatial coordinates and vertical spatial coordinates of a two dimensional plane;
assigning a temporal dimension across said image frames of said video stream, wherein said temporal dimension represents order of said image frames using predefined temporal coordinates;
constructing a spatio-temporal analysis image based on a user-defined line of analysis on each of one or more of said image frames, wherein said spatio-temporal analysis image is constructed by concatenating a series of temporally-successive linear pixel arrays along said temporal dimension, each of said linear pixel arrays comprising an array of pixels along said line of analysis defined on said each of said one or more of said image frames; and
segmenting said constructed spatio-temporal analysis image for capturing said events of interest.
2. The computer implemented method of claim 1, further comprising defining said line of analysis along one or more of said horizontal spatial coordinates, said vertical spatial coordinates, and a combination of said horizontal spatial coordinates and said vertical spatial coordinates in one or more of said image frames, by a user.
3. The computer implemented method of claim 1, wherein said constructed spatio-temporal analysis image is represented by one of a combination of a fixed horizontal spatial coordinate, a range of said vertical spatial coordinates and variable temporal coordinates, and a combination of a fixed vertical spatial coordinate, a range of said horizontal spatial coordinates and variable temporal coordinates.
4. The computer implemented method of claim 1, wherein said constructed spatio-temporal analysis image provides a summary of events occurring over said image frames at said line of analysis for one of a duration of said video and a duration of said concatenation of said series of said temporally-successive linear pixel arrays.
5. The computer implemented method of claim 1, wherein a width of each of said linear pixel arrays is substantially identical to a width of said line of analysis, wherein said width of said line of analysis is at least a single pixel.
6. The computer implemented method of claim 1, wherein said concatenation of said series of said temporally-successive linear pixel arrays along said temporal dimension comprises concatenating said linear pixel arrays from every Nth successive image frame in said series of image frames.
7. The computer implemented method of claim 1, further comprising defining one or more lines of analysis by a user for parallely constructing multiple spatio-temporal analysis images.
8. The computer implemented method of claim 1, wherein said segmentation of said constructed spatio-temporal analysis image comprises foreground segmentation for detecting objects and said events of interest, said foreground segmentation comprising:
modeling a background of said constructed spatio-temporal analysis image;
subtracting said modeled background from said constructed spatio-temporal analysis image for obtaining a foreground of said constructed spatio-temporal analysis image; and
performing segmentation of said obtained foreground for detecting said objects and said events of interest.
9. The computer implemented method of claim 1, wherein said capture of said events of interest at said line of analysis comprises detecting presence of an object, detecting traversal of said object, determining speed of said traversal of said object, determining an object count based on said traversal of one or more objects, and determining duration of said presence of said object.
10. The computer implemented method of claim 9, wherein said determination of said speed of said traversal of said object comprises:
receiving one or more lines of analysis spaced apart from each other by a separation distance on said image frames from a user, wherein said separation distance is based on an actual distance in an actual scene;
constructing one or more spatio-temporal analysis images based on said one or more lines of analysis;
determining presence and times of said presence of said object on said constructed one or more spatio-temporal analysis images using foreground segmentation; and
determining said speed of said traversal of said object based on said separation distance, frame rate of said video, and difference between times of said presence of said object on said constructed one or more spatio-temporal analysis images.
11. A computer implemented system for capturing events of interest by performing a spatio-temporal analysis of a video, comprising:
a moving image capture device that acquires a continuous video stream containing a series of image frames over time, wherein each of said image frames is represented by horizontal spatial coordinates and vertical spatial coordinates of a two dimensional plane;
a video content analyzer provided on a computing device for performing:
assigning a temporal dimension across said image frames of said video stream, wherein said temporal dimension represents order of said image frames using predefined temporal coordinates; and
constructing a spatio-temporal analysis image based on a user-defined line of analysis on each of one or more of said image frames, wherein said spatio-temporal analysis image is constructed by concatenating a series of temporally-successive linear pixel arrays along said temporal dimension, each of said linear pixel arrays comprising an array of pixels underlying said line of analysis defined on said each of said one or more of said image frames; and
an image segmentation module provided on said computing device for segmenting said constructed spatio-temporal analysis image for capturing said events of interest.
12. The computer implemented system of claim 11, further comprising a user interface on said computing device for defining said line of analysis along one or more of said horizontal spatial coordinates, said vertical spatial coordinates and a combination of said horizontal spatial coordinates and said vertical spatial coordinates in one or more of said image frames.
13. The computer implemented system of claim 12, wherein said user interface enables a user to define one or more lines of analysis for parallely constructing multiple spatio-temporal analysis images.
14. The computer implemented system of claim 11, wherein said constructed spatio-temporal analysis image is represented by one of a combination of a fixed horizontal spatial coordinate, a range of said vertical spatial coordinates, and variable temporal coordinates and a combination of a fixed vertical spatial coordinate, a range of horizontal spatial coordinates, and variable temporal coordinates.
15. The computer implemented system of claim 11, wherein said constructed spatio-temporal analysis image provides a summary of events occurring over said image frames at said line of analysis for one of a duration of said video and a duration of said concatenation of said series of said temporally-successive linear pixel arrays.
16. The computer implemented system of claim 11, wherein a width of each of said linear pixel arrays is substantially identical to a width of said line of analysis, wherein said width of said line of analysis is at least a single pixel.
17. The computer implemented system of claim 11, wherein said image segmentation module performs foreground segmentation of said constructed spatio-temporal analysis image for detecting objects and said events of interest, said image segmentation module comprising:
a background modeler for modeling a background of said constructed spatio-temporal analysis image;
a background subtractor for subtracting said modeled background from said constructed spatio-temporal analysis image for obtaining a foreground of said constructed spatio-temporal analysis image; and
a foreground segmentation module for performing segmentation of said obtained foreground for detecting said objects and said events of interest.
18. The computer implemented system of claim 11, wherein said captured events of interest at said line of analysis comprises presence of an object, traversal of said object, speed of said traversal of said object, an object count based on said traversal of one or more objects, and duration of said presence of an object.
19. A computer program product comprising computer executable instructions embodied in a non-transitory computer readable storage medium, wherein said computer program product comprises:
a first computer parsable program code for acquiring a continuous video stream containing a series of image frames over time, wherein each of said image frames is represented by horizontal spatial coordinates and vertical spatial coordinates of a two dimensional plane;
a second computer parsable program code for assigning a temporal dimension across said image frames of said video stream, wherein said temporal dimension represents order of said image frames using predefined temporal coordinates;
a third computer parsable program code for constructing a spatio-temporal analysis image based on a user-defined line of analysis on each of one or more of said image frames, wherein said spatio-temporal analysis image is constructed by concatenating a series of temporally-successive linear pixel arrays along said temporal dimension, each of said linear pixel arrays comprising an array of pixels along said line of analysis defined on each of one or more of said image frames; and
a fourth computer parsable program code for segmenting said spatio-temporal analysis image for capturing said events of interest.
20. The computer program product of claim 19, wherein said fourth computer parsable program code for segmenting said spatio-temporal analysis image comprises:
a fifth computer parsable program code for modeling a background of said constructed spatio-temporal analysis image;
a sixth computer parsable program code for subtracting said modeled background from said constructed spatio-temporal analysis image for obtaining a foreground of said constructed spatio-temporal analysis image; and
a seventh computer parsable program code for performing segmentation of said obtained foreground for detecting said objects and said events of interest.
This application claims the benefit of non-provisional patent application number 1753/CHE/2010 titled “Capturing Events Of Interest By Spatio-temporal Video Analysis”, filed on Jun. 23, 2010 in the Indian Patent Office.
Video content analysis (VCA) refers to processing of video for determining events of interest and activity in a video, such as, a count of people or vehicles passing through a zone, the direction and speed of their movement, breach of boundary, speeding vehicles, etc. using computer vision techniques. VCA finds numerous applications in video surveillance, customer behavior in super markets, vehicular traffic analysis, and other areas. VCA is becoming a necessity given the extent of breach of security, requirement for surveillance, and threats to or potential compromise of human life and property in cities, defense establishments and at industrial and commercial premises.
Existing VCA algorithms process an entire image or a patch of the image using pure spatial image processing of the video. Hence, these algorithms are inherently suboptimal with respect to their computational efficiency and memory utilization. For example, processing visual graphics array (VGA) image frames of 640×480 pixels each in pure spatial domain requires segmentation of the entire 307200 pixels of each image frame, which is highly memory intensive.
Hence, there is a long felt but unresolved need for a computer implemented method and system for capturing events of interest by performing spatio-temporal analysis of a video using user-defined lines of analysis.
The computer implemented method and system disclosed herein addresses the above stated need for capturing events of interest by optimally performing spatio-temporal analysis of a video using one or more user-defined lines of analysis. A continuous video stream containing a series of image frames is acquired over time. Each of the image frames is represented by horizontal spatial coordinates and vertical spatial coordinates of a two dimensional (2D) plane. A temporal dimension is assigned across the image frames of the video stream. The temporal dimension represents the order of the image frames using predefined temporal coordinates. A spatio-temporal analysis image is constructed based on a user-defined line of analysis on each of one or more of the image frames. The spatio-temporal analysis image is constructed by concatenating a series of temporally-successive linear pixel arrays along the temporal dimension. Each of the linear pixel arrays comprises an array of pixels selected along the line of analysis defined on each of one or more of the image frames. For example, the spatio-temporal analysis image is constructed by concatenating linear pixel arrays from every Nth successive image frame in the series of image frames. The width of each of the linear pixel arrays is substantially identical to the width of the line of analysis. The line of analysis is, for example, at least a single pixel wide. The constructed spatio-temporal analysis image is segmented for capturing the events of interest. The constructed spatio-temporal analysis image provides a summary of events occurring over the image frames at the line of analysis for the duration of the video or the duration of the concatenation of the series of temporally-successive linear pixel arrays. A user can define one or more lines of analysis for parallely constructing multiple spatio-temporal analysis images that are related in time.
The capture of the events of interest at the line of analysis comprises, for example, detecting presence of an object, detecting traversal of an object, determining speed of the traversal of the object, determining an object count based on the traversal of one or more objects, and determining duration of the presence of the object.
A user defines the line of analysis, having any orientation in the 2D plane, on one or more of the image frames. The user-inputted line of analysis on one of the image frames is used to automatically accumulate the linear pixel arrays from the remaining successive image frames by replicating the same coordinates of the line of analysis over these image frames. Accordingly, the line of analysis is defined by the horizontal spatial coordinates, or the vertical spatial coordinates, or by a combination of the horizontal spatial coordinates and the vertical spatial coordinates in each of the image frames. The constructed spatio-temporal analysis image is represented by a fixed spatial coordinate, a range of variable spatial coordinates, and variable temporal coordinates. For example, the constructed spatio-temporal analysis image is represented by a combination of a fixed horizontal spatial coordinate, a range of the vertical spatial coordinates and variable temporal coordinates, or a combination of a fixed vertical spatial coordinate, a range of the horizontal spatial coordinates and variable temporal coordinates.
The segmentation of the constructed spatio-temporal analysis image comprises foreground segmentation for detecting objects and the events of interest. A background of the constructed spatio-temporal analysis image is modeled, for example, by determining a moving average of the linear pixel arrays along the temporal dimension. The modeled background is subtracted from the constructed spatio-temporal analysis image for obtaining the foreground of the constructed spatio-temporal analysis image. Segmentation of the obtained foreground is then performed for detecting the objects and the events of interest.
Different events of interest are captured using the spatio-temporal analysis image based on the actual events occurring in the actual scenario being captured. One of the captured events is the speed of traversal of the object, for example, a moving vehicle. The speed of traversal of the object is determined by receiving one or more lines of analysis spaced apart from each other by a separation distance on the image frames from a user. The separation distance is based on an actual distance in the actual scene being captured. One or more spatio-temporal analysis images are constructed based on the lines of analysis. The presence and the times of presence of the object on the constructed spatio-temporal analysis images are determined using foreground segmentation. The speed of traversal of the object is determined based on the separation distance, frame rate of the video, and difference between times of presence of the object on the constructed spatio-temporal analysis images.
FIG. 1 illustrates a computer implemented method for capturing events of interest by performing spatio-temporal analysis of a video.
FIG. 2A exemplarily illustrates a representation of a cube of image frames over time.
FIG. 2B exemplarily illustrates a spatio-temporal analysis image having spatial coordinates and temporal coordinates.
FIG. 3 exemplarily illustrates foreground segmentation of the spatio-temporal analysis image for detecting objects and events of interest.
FIG. 4A exemplarily illustrates sample user-defined lines of analysis in different inclinations.
FIG. 4B exemplarily illustrates a user-defined line of analysis intersecting elements in an image frame.
FIG. 4C exemplarily illustrates a spatio-temporal analysis image constructed based on the line of analysis depicted in FIG. 4B.
FIG. 4D exemplarily illustrates a modeled background of the spatio-temporal analysis image depicted in FIG. 4C.
FIG. 4E exemplarily illustrates a segmented foreground of the spatio-temporal analysis image depicted in FIG. 4C.
FIG. 5 illustrates a computer implemented system for capturing events of interest by performing a spatio-temporal analysis of a video.
FIG. 6 exemplarily illustrates the architecture of a computer system used for capturing events of interest by performing a spatio-temporal analysis of a video.
FIG. 7A exemplarily illustrates a screenshot of a spatio-temporal analysis image constructed based on a user-defined line of analysis.
FIG. 7B exemplarily illustrates a binary difference image of FIG. 7A.
FIG. 7C exemplarily illustrates foreground segmentation of the spatio-temporal analysis image of FIG. 7A.
FIG. 8 exemplarily illustrates a computer implemented method for determining speed of traversal of an object using spatio-temporal analysis of a video.
FIG. 1 illustrates a computer implemented method for capturing events of interest by performing spatio-temporal analysis of a video. A continuous video stream containing a series of image frames is acquired 101 over time. Each of the image frames is represented by horizontal spatial coordinates (X) and vertical spatial coordinates (Y) of a two dimensional (2D) plane. A temporal dimension (T) is assigned 102 across the image frames of the video stream. The temporal dimension (T) represents the order of the image frames using predefined temporal coordinates. A spatio-temporal analysis image is constructed 103 based on a user-defined line of analysis on each of one or more of the image frames. As used herein, the term “line of analysis” refers to a selection of a linear array of pixels in each moving image frame. The linear array of pixels in every line of analysis is sequentially accumulated over time in order to analyze and summarize events that are exposed only to the accumulated linear arrays of pixels. The width of each of the linear pixel arrays is substantially identical to the width of the line of analysis. The line of analysis is, for example, at least a single pixel wide. The spatio-temporal analysis image is constructed by concatenating a series of temporally-successive linear pixel arrays along the temporal dimension. Each of the linear pixel arrays comprises an array of pixels selected along the line of analysis defined on each of one or more of the image frames. For example, the spatio-temporal analysis image is constructed by concatenating linear pixel arrays from every Nth successive image frame in the series of image frames. The constructed spatio-temporal analysis image is segmented 104 for capturing the events of interest. The constructed spatio-temporal analysis image provides a summary of events occurring over the image frames at the line of analysis for the duration of the video or the duration of the concatenation of the series of temporally-successive linear pixel arrays. A user can define one or more lines of analysis for parallel construction of multiple spatio-temporal analysis images that are related in time.
The capture of events of interest at the line of analysis comprises, for example, detecting presence of an object, detecting traversal of the object, determining speed of the traversal of the object, determining an object count based on the traversal of one or more objects, determining duration of presence of the object, etc.
The user defines the line of analysis, having any in-plane orientation with respect to the two dimensional (2D) plane, on one or more of the image frames. The user-inputted line of analysis on one of the image frames is used to automatically accumulate the linear pixel arrays from the remaining successive image frames by replicating the same coordinates of the line of analysis over these image frames. Accordingly, the line of analysis is defined by the horizontal spatial coordinates or the vertical spatial coordinates, or by a combination of the horizontal spatial coordinates and the vertical spatial coordinates in one or more of the image frames. The constructed spatio-temporal analysis image is represented by a fixed spatial coordinate, a definite range of variable spatial coordinates and variable temporal coordinates. For example, the constructed spatio-temporal analysis image is represented by a combination of a fixed horizontal spatial coordinate, a range of vertical spatial coordinates and variable temporal coordinates, or a combination of a fixed vertical spatial coordinate, a range of horizontal spatial coordinates and variable temporal coordinates.
A normal static 2D image frame can be expressed as lines arranged horizontally or vertically such that each line is obtained from pixels Pij at j=n for all values of i or i=m for all values of j to a maximum number of rows or columns. The static 2D image frame comprises pure spatial data in XY coordinates. Similarly, a video stream comprising 2D static image frames stacked in time (T) is visualized as a cube 201 of image frames, wherein the third dimension or the transverse dimension is time (T). FIG. 2A exemplarily illustrates a representation of a cube 201 of image frames over time. The video stream can thus be visualized as an image cube 201 in XYT coordinates. A slice of the image cube 201 at a point on the X-axis, for example, xi or on the Y-axis, for example, yj over all values of time T, results in a reconstructed image that resembles a normal 2D static image, but reproduced in XT coordinates or YT coordinates. The line that intersects x, for all values of Y and T, or the line that intersects yj for all values of X and T is referred to as the “line of analysis” (LOA) 202.
FIG. 2B exemplarily illustrates a spatio-temporal analysis image 203 having spatial and temporal coordinates. The spatio-temporal analysis image 203 exhibits certain visual characteristics that are interpreted to detect events of interest in any scenario. The relevant events of video surveillance, for example, perimeter breach, movement of objects, removal or addition of objects to a scene, etc., leaves a signature in space when captured and visualized over time (T). The spatio-temporal video content analysis (VCA) in XYT coordinates according to the computer implemented method disclosed herein extracts such signatures and performs pattern recognition over those signatures for detecting objects and events of interest.
Mathematically, each individual frame of the moving images is represented by I(y, x) and the video stream is represented by I(y, x, t), where x, y and t represent columns, rows and a time-axis, respectively, with the assumption that “x” varies from 0 to N, “y” varies from 0 to M, and “t” varies from 0 to R. A user selects the line of analysis 202 by providing coordinates of the end points of the line 202 using a user interface, for example, a graphical user interface or a textual user interface. The user is presented with one of the image frames, for example, I(y, x, 3) which is the image frame at t=3, as exemplarily illustrated in FIG. 4A, on which the user selects the line of analysis (LOA) 202 a, 202 b, or 202 c. FIG. 4A exemplarily illustrates sample user-defined lines of analysis 202 a, 202 b, and 202 c in different in-plane inclinations with the respect to the 2D plane.
In an example, if the line of analysis 202 a is selected along row number 10 and entire “x”, the resulting spatio-temporal analysis image 203 is given by I(10, x, t), where “x” varies from 0 to N, and “t” varies from 0 to R. The values of “x” can be specified by a range “x1” to “x2”, providing the image I(10, β, t), where “β” varies from x1 to x2, and “t” varies from 0 to R. In another example, if the line of analysis 202 b is selected along column number 23 and entire “y”, the resulting spatio-temporal analysis image 203 is mathematically given by I(y, 23, t), where “y” varies from 0 to M, and “t” varies from 0 to R. The values of “y” can be specified within a range “y1” to “y2”, providing the image I(α, 23, t), where “α” varies from y1 to y2, and “t” varies from 0 to R. Similarly, for a line of analysis 202 c selected between any two points, at any inclination and length within the image resolution results in an spatio-temporal analysis image I(α, β, t), where (α, β) specifies the points on the line of analysis 202 c varying between y and x values of the two end-points and “t” varies from 0 to R. In an embodiment, the inclined linear arrays of pixels along the line of analysis 202 c from the image frames are re-arranged to form vertical lines on the spatio-temporal analysis image 203, yielding a vertical, that is column, spatial axis and a horizontal, that is row, time axis. Alternatively, the inclined linear arrays of pixels along the line of analysis 202 c from the image frames are re-arranged to form horizontal lines on the spatio-temporal analysis image 203, thereby yielding a horizontal spatial axis and a vertical time axis.
Consider an example where the user selects a vertical line of analysis 202 b, at a point on the x-axis, which varies over the entirety of the y-axis, that is, from 0 to M, as exemplarily illustrated in FIG. 4B. FIG. 4B exemplarily illustrates a user-defined line of analysis 202 b that intersects elements, for example, both sides of a street in the image frame. The array of pixels over this line of analysis 202 b is accumulated over time “t”, for example, over 250 image frames for 10 seconds to construct a spatio-temporal analysis image 203 as exemplarily illustrated FIG. 4C. FIG. 4C exemplarily illustrates a spatio-temporal analysis image 203 constructed based on the line of analysis 202 b depicted in FIG. 4B. This spatio-temporal analysis image 203 is mathematically represented by a fixed horizontal spatial coordinate (x), vertical variable coordinates (y), for example, y1 to y2, and variable temporal coordinates (t). This spatio-temporal analysis image 203 has a resolution of L×T, where “L” is the length of the line of analysis 202 b and “T” is the product of time of analysis in seconds and frame rate of the video.
FIG. 4C also illustrates three horizontal lines and a human object 401 on the spatio-temporal analysis image 203. The three horizontal lines running along time “t” appear in the spatio-temporal analysis image 203 due to the intersection of the line of analysis 202 b with the street borders and the street median. The human object 401 is reproduced in the spatio-temporal analysis image 203, since the human object 401 crosses or moves across the line of analysis 202 b, exposing itself as a linear pixelled array on each image frame which are accumulated over time “t”. The spatio-temporal analysis image 203 exemplarily illustrated in FIG. 4C is a summary of events occurring over 250 image frames or 10 seconds of the video along the line of analysis 202 b, and is used for visualization and segmentation for detecting events of interest.
A moving image capture device, for example, a closed-circuit television (CCTV) camera used for surveillance is mounted on rooftops or in tunneling toll booths and may be adapted for panning over a limited sweep angle or subjected to undue jitter. The linear array of pixels selected from each image frame based on the line of analysis 202 reproduces a specific section of the actual image captured at a single instance of time (t). In an embodiment, where the camera is panning over a limited sweep angle, the coordinates of the line of analysis 202 may be dynamically shifted to continue to focus the analysis at a specific part of the actual scene. The coordinates of the line of analysis 202 may be dynamically shifted by obtaining the pan direction and pan magnitude during every transition from one image frame to another. Also, the coordinates of the line of analysis 202 may be dynamically shifted by obtaining the average pan direction and pan magnitude, or the velocity over a single sweep of the camera. This ensures that the linear array of pixels extracted from each successive image frame represents identical section or part of the actual scene, when the moving image capture device is panning over a limited sweep angle. This however requires that the focused part(s) of the scene always remains within the field of view of the camera. Where the camera may be subjected to a momentary jitter, a wider line of analysis 202 is used to compensate for the jitter. Additionally or alternatively, the spatio-temporal analysis image 203 may be constructed by concatenating linear pixel arrays from every Nth successive image frame, for example, every 3rd successive frame in the series of image frames, drowning out momentary fluctuations in the constructed spatio-temporal analysis image 203.
FIG. 3 exemplarily illustrates foreground segmentation of the spatio-temporal analysis image 203 for detecting objects and events of interest. A background of the constructed spatio-temporal analysis image 203 is modeled 301, for example, by determining a moving average of the linear pixel arrays along the temporal dimension (t). The modeled background is subtracted 302 from the constructed spatio-temporal analysis image 203 for obtaining the foreground of the constructed spatio-temporal analysis image 203. The segmentation of the obtained foreground is performed 303 for detecting objects and events of interest.
The video content analysis of the spatio-temporal analysis image 203 using foreground segmentation may be performed using different techniques for background modeling and segmentation of the constructed spatio-temporal analysis image 203. The background modeling and segmentation according to the computer implemented method disclosed herein employs, for example, the moving average technique to determine the moving average of each vertical line of the spatio-temporal analysis image 203 of FIG. 4C. Each vertical line in the spatio-temporal analysis image 203 of FIG. 4C corresponds to the line of analysis 202 an image frame in the video at a specific instance of time, which is used to construct the spatio-temporal analysis image 203. Mathematically, the spatio-temporal analysis image 203 of FIG. 4C can be represented as follows:
I(y,α,t);
where I(y, α, t) is taken at x=α, where a is a value in the range of “x” between 0 to N, “y” varies from 0 to M and “t” varies from 0 to R.
The background of the spatio-temporal analysis image 203 is determined by applying a weighted moving average of the linear pixel arrays over time “t”. To begin with, the background of the image frame at “t0” corresponding to the linear pixel array in the spatio-temporal analysis image 203 at “t0” is modeled as follows:
B(y,α,t0)=I(y,α,t0);
Similarly, B(y, α, t1)=I(y, α, t1)*δ+B(y, α, t0)*(1−δ), where “δ” is a background adapting factor and is always 0≦δ≦1; and “t0” and “t1” are the either absolute or relative time indices, representing the image frames from which the linear arrays of pixels are taken. “(y, α, t1)” corresponds to the linear array taken at “α” column (LOA), for selected range of rows “y” on the image frame at time “t1”. B(y, α, t0) is the background modeled at image frame “t0” and can be a line array with all zeros. B(y, α, t1) is the background modeled at image frame “t1”. The background image, exemplarily illustrated in FIG. 4D, is generated by similarly modeling the background up to time tR, with the background being optionally modeled at each time instance (t0 to tR) in real time, as the spatio-temporal analysis image 203 is constructed.
The foreground is segmented by obtaining the absolute difference between the spatio-temporal analysis image 203 of FIG. 4C and the background image of FIG. 4D, and thresholding the differences. For example, segmentation on the spatio-temporal analysis image 203 at “t1” is performed by taking the absolute of the difference between I(y, α, t1) and B(y, α, t1), and thresholding the difference as follows:
D(y,α,t1)=1 if “absolute(B(y,α,t1)−I(y,α,t1))≧threshold;
D(y,α,t1)=0 if “absolute(B(y,α,t1)−I(y,α,t1))<threshold;
The segmented image SI(y, t) is obtained by accumulating D(y, α, t0), D(y, α, t1), D(y, α, t2) . . . so on till D(y, α, tR), since “t” varies from 0 to R. The foreground segmented image SI(y, t) is illustrated in FIG. 4E.
The spatio-temporal or XYT analysis provides a background model using a single line of pixels as opposed to the conventional background models that process the entire image. Given a video graphics array (VGA) image of 640×480 pixels, the background modeling and segmentation according to the computer implemented method disclosed herein processes 480 pixels for analysis instead of the entire 307200 pixels. The line of analysis 202 is, for example, one pixel wide and hence precludes any duplication of objects, unless the objects cross the line of analysis 202 more than once. The spatio-temporal analysis for video content is, for example, used for people or vehicle count with better accuracy. The XYT analysis summarizes the video of a few gigabytes (GB) for monitoring a highway, a tollgate, an entrance to an office, or an industry or commercial premises, etc., in a few megabytes (MB) of memory. The spatio-temporal analysis image 203 is used for visualization, with each line along the time axis (t) of the spatio-temporal analysis image 203 specifying the unit of time that is used for time indexing for navigating through the video.
The spatio-temporal analysis image 203 constructed by the spatio-temporal analysis of the video exhibits a few notable unique characteristics. For example, if an object remains stationary on the line of analysis 202, the reproduction of the object in the spatio-temporal analysis image appears stretched or elongated along the temporal dimension (t). On the other hand, if an object traverses the line of analysis 202 at a high speed, the object appears compressed in the spatio-temporal analysis image. The spatio-temporal analysis according to the computer implemented method disclosed herein is a valuable technique to summarize an entire day's traffic at a toll gate and the shopper traffic at the entrance of a mall, to determine traffic line violations, the speed of vehicles, perimeter breach in a mining area, industrial or commercial campuses, etc.
FIG. 5 illustrates a computer implemented system 500 for capturing events of interest by performing a spatio-temporal analysis of a video. The computer implemented system 500 disclosed herein comprises a moving image capture device 502 and a computing device 501. The computing device 501 of the computer implemented system 500 disclosed herein comprises a video content analyzer 501 a, an image segmentation module 501 b, and a user interface 501 f. The moving image capture device 502, for example, a surveillance camera acquires a continuous video stream containing a series of image frames over time. Each of the image frames is represented by horizontal spatial coordinates and vertical spatial coordinates of a two dimensional plane. The moving image capture device 502 may be connected to a network 503, for example, a local area network to transfer the acquired moving images over the network 503 to the computing device 501. In an embodiment, the moving device capture device 502 is connected to the computing device 501 via serial and parallel communication ports, for example, using a universal serial bus (USB) specification. In another embodiment, the moving image capture device 502 may comprise a dedicated processor and memory for installing and executing the video content analyzer 501 a, the image segmentation module 501 b, and the user interface 501 f.
The video content analyzer 501 a provided on the computing device 501 assigns a temporal dimension across the image frames of the video stream. The temporal dimension represents the order of the image frames using predefined temporal coordinates. The video content analyzer 501 a constructs a spatio-temporal analysis image 203 based on a user-defined line of analysis 202 on each of the image frames. The video content analyzer 501 a constructs the spatio-temporal analysis image 203 by concatenating a series of temporally-successive linear pixel arrays along the temporal dimension. Each of the linear pixel arrays comprises an array of pixels underlying the line of analysis 202 defined on each of the image frames. The image segmentation module 501 b on the computing device 501 segments the constructed spatio-temporal analysis image 203 for capturing the events of interest.
The user interface 501 f, for example, a graphical user interface or a textual user interface on the computing device 501 renders one or more image frames of a stored video or a live video to the user. The user interface 501 f enables the user to define the line of analysis 202, having any in-plane orientation, length and width, on the image frames. In an embodiment, the user interface 501 f enables the user to define one or more lines of analysis 202 for parallely constructing multiple spatio-temporal analysis images. The constructed spatio-temporal analysis image 203 provides a summary of events occurring over the image frames at the line of analysis 202 for the duration of the video or the duration of the concatenation of the series of temporally-successive linear pixel arrays. The spatio-temporal analysis image 203 is represented by, for example, a combination of a fixed horizontal spatial coordinate, a range of the vertical spatial coordinates, and variable temporal coordinates, or a combination of a fixed vertical spatial coordinate, a range of the horizontal spatial coordinates, and variable temporal coordinates.
The image segmentation module 501 b performs foreground segmentation of the constructed spatio-temporal analysis image 203 for detecting objects and events of interest. The image segmentation module 501 b comprises a background modeler 501 c, a background subtractor 501 d, and a foreground segmentation module 501 e. The background modeler 501 c models the background of the constructed spatio-temporal analysis image 203, for example, by determining a moving average of the linear pixel arrays along the temporal dimension. The background subtractor 501 d subtracts the modeled background from the constructed spatio-temporal analysis image 203 by obtaining the absolute difference between the constructed spatio-temporal analysis image 203 and the modeled background for obtaining foreground of the spatio-temporal analysis image 203. The foreground segmentation module 501 e performs segmentation of the obtained foreground for detecting objects and events of interest, for example, presence of an object, traversal of the object, speed of traversal of the object, an object count based on the traversal of one or more objects, duration of the presence of an object, etc.
FIG. 6 exemplarily illustrates the architecture of a computer system 600 used for capturing events of interest by performing a spatio-temporal analysis of a video. The computer system 600 comprises a processor 601, a memory unit 602 for storing programs and data, an input/output (I/O) controller 603, and a display unit 606 communicating via a data bus 605. The memory unit 602 comprises a random access memory (RAM) and a read only memory (ROM). The computer system 600 comprises one or more input devices 607, for example, a keyboard such as an alphanumeric keyboard, a mouse, a joystick, etc. The input/output (I/O) controller 603 controls the input and output actions performed by a user. The computer system 600 communicates with other computer systems through an interface 604, for example, a Bluetooth™ interface, an infrared (IR) interface, a WiFi interface, a universal serial bus interface (USB), a local area network (LAN) or wide area network (WAN) interface, etc.
The processor 601 is an electronic circuit that can execute computer programs. The memory unit 602 is used for storing programs, applications, and data. For example, the video content analyzer 501 a and the image segmentation module 501 b are stored on the memory unit 602 of the computer system 600. The memory unit 602 is, for example, a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processor 601. The memory unit 602 also stores temporary variables and other intermediate information used during execution of the instructions by the processor 601. The computer system 600 further comprises a read only memory (ROM) or another type of static storage device that stores static information and instructions for the processor 601. The data bus 605 permits communication between the modules, for example, 501 a, 501 b, 501 c, 501 d, 501 e, and 501 f of the computer implemented system 500 disclosed herein.
Computer applications and programs are used for operating the computer system 600. The programs are loaded onto the fixed media drive 608 and into the memory unit 602 of the computer system 600 via the removable media drive 609. In an embodiment, the computer applications and programs may be loaded directly through the network 503. Computer applications and programs are executed by double clicking a related icon displayed on the display unit 606 using one of the input devices 607. The user interacts with the computer system 600 using a user interface 501 f of the display unit 606. The user selects the line of analysis 202 of any orientation, size, and thickness on one or more image frames on the user interface 501 f of the display unit 606 using one of the input devices 607, for example, a computer mouse.
The computer system 600 employs an operating system for performing multiple tasks. The operating system manages execution of, for example, the video content analyzer 501 a and the image segmentation module 501 b provided on the computer system 600. The operating system further manages security of the computer system 600, peripheral devices connected to the computer system 600, and network connections. The operating system employed on the computer system 600 recognizes keyboard inputs of a user, output display, files and directories stored locally on the fixed media drive 608, for example, a hard drive. Different programs, for example, a web browser, an e-mail application, etc., initiated by the user are executed by the operating system with the help of the processor 601, for example, a central processing unit (CPU). The operating system monitors the use of the processor 601.
The video content analyzer 501 a and the image segmentation module 501 b are installed in the computer system 600 and the instructions are stored in the memory unit 602. The captured moving images are transferred from the moving image capture device 502 to the video content analyzer 501 a installed in the computer system 600 of the computing device 501 via the interface 604 or a network 503. A user initiates the execution of the video content analyzer 501 a by double clicking the icon for the video content analyzer 501 a on the display unit 606 or the execution of the video content analyzer 501 a is automatically initiated on installing the video content analyzer 501 a on the computing device 501. Instructions for capturing events of interest by performing spatio-temporal analysis of the video are retrieved by the processor 601 from the program memory in the form of signals. The locations of the instructions from the modules, for example, 501 a, 501 b, 501 c, 501 d, and 501 e, are determined by a program counter (PC). The program counter stores a number that identifies the current position in the program of the video content analyzer 501 a and the image segmentation module 501 b.
The instructions fetched by the processor 601 from the program memory after being processed are decoded. The instructions are placed in an instruction register (IR) in the processor 601. After processing and decoding, the processor 601 executes the instructions. For example, the video content analyzer 501 a defines instructions for assigning a temporal dimension across the image frames of the video stream. The video content analyzer 501 a further defines instructions for constructing a spatio-temporal analysis image 203 based on a user-defined line of analysis 202 on each of the image frames. The image segmentation module 501 b defines instructions for segmenting the constructed spatio-temporal analysis image 203 for capturing the events of interest. The background modeler 501 c defines instructions for modeling the background of the constructed spatio-temporal analysis image 203. The background subtractor 501 d defines instructions for subtracting the modeled background from the constructed spatio-temporal analysis image 203 for obtaining the foreground of the constructed spatio-temporal analysis image 203. The foreground segmentation module 501 e defines instructions for performing segmentation of the obtained foreground for detecting objects and events of interest. The instructions are stored in the program memory or received from a remote server.
The processor 601 retrieves the instructions defined by the video content analyzer 501 a, the image segmentation module 501 b, the background modeler 501 c, the background subtractor 501 d, and the foreground segmentation sub-module 501 e, and executes the instructions.
At the time of execution, the instructions stored in the instruction register are examined to determine the operations to be performed. The specified operation is then performed by the processor 601. The operations include arithmetic and logic operations. The operating system performs multiple routines for performing a number of tasks required to assign input devices 607, output devices 610, and memory for execution of the video content analyzer 501 a and the image segmentation module 501 b. The tasks performed by the operating system comprise assigning memory to the video content analyzer 501 a, the image segmentation module 501 b and data, moving data between the memory 602 and disk units and handling input/output operations. The operating system performs the tasks on request by the operations and after performing the tasks, the operating system transfers the execution control back to the processor 601. The processor 601 continues the execution to obtain one or more outputs. The outputs of the execution of the video content analyzer 501 a and the image segmentation module 501 b are displayed to the user on the display unit 606.
FIG. 7A exemplarily illustrates a screenshot of a spatio-temporal analysis image constructed based on a user-defined line of analysis 202. As illustrated in FIG. 7A, the spatio-temporal analysis image is constructed based on a vertical line of analysis 202 b selected at a point on the x-axis (Xi), which varies over the entirety of the y-axis. FIG. 7B exemplarily illustrates a binary difference image of FIG. 7A. FIG. 7C exemplarily illustrates foreground segmentation of the spatio-temporal analysis image of FIG. 7A. The foreground segmentation reveals that four pedestrians and a vehicle have passed the line of analysis 202 b over a finite time period T of the video being analyzed.
The spatio-temporal analysis of a video also referred to as “video content analysis” according to the computer implemented method and system 500 disclosed herein for constructing and segmenting the spatio-temporal analysis image provides different video content analysis applications for monitoring and surveillance in different scenarios. A few applications in different scenarios are disclosed herein. The spatio-temporal analysis is used to determine a count of people over a predetermined duration. The people count is determined by the count of foreground objects in the spatio-temporal analysis image. The spatio-temporal analysis image is constructed by accumulating the linear pixels arrays from the image frames of the video, based on the line of analysis 202, over the predetermined duration. In this case, the line of analysis 202 is defined across a doorway, a passageway, or a corridor dominated by incoming and outgoing people.
In another scenario, where the line of analysis 202 is selected to depict a perimeter line, the spatio-temporal analysis at the line of analysis 202 is used to detect a perimeter breach or simulate a tripwire. When an object crosses the line of analysis 202, the object is reproduced in the spatio-temporal analysis image 203, as illustrated in FIG. 4C. The spatio-temporal analysis image is generated over time and segmented to determine the number of perimeter breaches and the time of perimeter breaches.
In another scenario, the speed of traversal of the object, for example, a moving vehicle is determined using one or more spatio-temporal analysis images. FIG. 8 exemplarily illustrates a computer implemented method for determining speed of traversal of an object using spatio-temporal analysis of a video. The speed of an object is determined by receiving 801 one or more lines of analysis 202, for example, at Xi and Xk spaced apart from each other by a separation distance on the image frames from a user. The separation distance is based on an actual distance in the actual scene being captured. One or more spatio-temporal analysis images are constructed 802 based on the lines of analysis 202. The presence and the times of presence of the object on the constructed spatio-temporal analysis images, for example, XiYT and XkYT are determined 803 using foreground segmentation disclosed in the detailed description of FIG. 3. The speed of traversal of the object is determined 804 based on the separation distance, frame rate of the video and difference between times of presence of the object on the constructed spatio-temporal analysis images.
Other applications based on similar principles include, for example, determining the winner in a track race. In this case, the line of analysis 202 is defined along the finish line of a track. A spatio-temporal analysis image is constructed based on the line of analysis 202 to determine the race winner based on the time of appearance of the participants or athletes on the spatio-temporal analysis image. The spatio-temporal analysis disclosed herein also enables automated parking lot management. The length of the stretch or elongation of an object, for example, a stationary vehicle on the spatio-temporal analysis image provides the parking duration of the stationary vehicle. The line of analysis 202 is defined within the parking slot with a suitable orientation and length. In the same way, the vacant slots in a parking lot are determined by considering the binary difference image of the spatio-temporal (XYT) image of the parking lot. For example, white patches on the binary difference image correspond to occupied slots, while black patches correspond to the unoccupied slots. In road traffic management, where video monitoring is installed in the area around traffic lights and intersections, the line of analysis 202 is suitably defined along the road markings to enable video content analysis. At an unmanned railway crossing installed with video surveillance, the line of analysis 202 is defined along the rail track for video content analysis. In a museum with video surveillance for monitoring exhibits such as paintings or statues, video content analysis is performed at specific parts of the scene by defining the line of analysis 202 at those parts.
The events of interest in a scene may be specific to the application scenario and requirement, and the general arrangement or landscape of the captured scene. A person of ordinary skill in the art or any user in the monitoring and surveillance domain can easily recognize parts of a scene that require content analysis, and accordingly define one or more lines of analysis 202 on one or more image frames, including the orientation, length and width of the lines of analysis 202, for performing spatio-temporal analysis.
It will be readily apparent that the various methods and algorithms described herein may be implemented in a computer readable medium appropriately programmed for general purpose computers and computing devices. Typically a processor, for example, one or more microprocessors will receive instructions from a memory or like device, and execute those instructions, thereby performing one or more processes defined by those instructions. Further, programs that implement such methods and algorithms may be stored and transmitted using a variety of media, for example, computer readable media in a number of manners. In one embodiment, hard-wired circuitry or custom hardware may be used in place of, or in combination with, software instructions for implementation of the processes of various embodiments. Thus, embodiments are not limited to any specific combination of hardware and software. A “processor” means any one or more microprocessors, central processing unit (CPU) devices, computing devices, microcontrollers, digital signal processors or like devices. The term “computer readable medium” refers to any medium that participates in providing data, for example instructions that may be read by a computer, a processor or a like device. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks and other persistent memory volatile media include dynamic random access memory (DRAM), which typically constitutes the main memory. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to the processor. Common forms of computer readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a compact disc-read only memory (CD-ROM), digital versatile disc (DVD), any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a random access memory (RAM), a programmable read only memory (PROM), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), a flash memory, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read. In general, the computer readable programs may be implemented in any programming language. Some examples of languages that can be used include C, C++, C#, Perl, Python, or JAVA. The software programs may be stored on or in one or more mediums as an object code. A computer program product comprising computer executable instructions embodied in a computer readable medium comprises computer parsable codes for the implementation of the processes of various embodiments.
The present invention can be configured to work in a network environment including a computer that is in communication, via a communications network, with one or more devices. The computer may communicate with the devices directly or indirectly, via a wired or wireless medium such as the Internet, a local area network (LAN), a wide area network (WAN) or the Ethernet, token ring, or via any appropriate communications means or combination of communications means. Each of the devices may comprise computers, such as those based on the Intel® processors, AMD® processors, UltraSPARC® processors, Sun® processors, IBM° processors, etc. that are adapted to communicate with the computer. Any number and type of machines may be in communication with the computer.
US5784023 * 26 juni 1995 21 juli 1998 Bluege; John Speed detection method
US6529132 * 8 nov 2001 4 maart 2003 Societe Industrielle D'avation Latecoere Device for monitoring an enclosure, in particular the hold of an aircraft
US6856696 * 10 sept 1999 15 feb 2005 Ecchandes Inc Visual device
US7203693 * 12 juni 2002 10 april 2007 Lucent Technologies Inc. Instantly indexed databases for multimedia content analysis and retrieval
US7304649 * 27 juli 2005 4 dec 2007 Kabushiki Kaisha Toshiba Object region data describing method and object region data creating apparatus
US7347555 * 22 dec 2004 25 maart 2008 Micoy Corporation Multi-dimensional imaging apparatus, systems, and methods
US7356164 * 30 mei 2003 8 april 2008 Lucent Technologies Inc. Method and apparatus for finding feature correspondences between images captured in real-world environments
US7429997 * 28 nov 2001 30 sept 2008 Micoy Corporation System and method for spherical stereoscopic photographing
US7450165 * 30 april 2004 11 nov 2008 Grandeye, Ltd. Multiple-view processing in wide-angle video camera
US7519907 4 aug 2003 14 april 2009 Microsoft Corp. System and method for image editing using an image stack
US7528881 * 30 april 2004 5 mei 2009 Grandeye, Ltd. Multiple object processing in wide-angle video camera
US7545516 * 1 dec 2006 9 juni 2009 University Of Waterloo Full-field three-dimensional measurement method
US7856137 * 22 juni 2005 21 dec 2010 Glory Ltd. Apparatus and method for verifying image by comparison with template image
US7872665 * 13 mei 2005 18 jan 2011 Micoy Corporation Image capture and processing
US7876325 * 4 juni 2007 25 jan 2011 Adobe Systems Incorporated Effect transitioning based on key locations in spatial dimensions
US7949295 * 18 aug 2005 24 mei 2011 Sri International Automated trainee monitoring and performance evaluation system
US7949474 * 3 juni 2009 24 mei 2011 The Regents Of The University Of California Visual-servoing optical microscopy
US7957565 * 28 maart 2008 7 juni 2011 Videomining Corporation Method and system for recognizing employees in a physical space based on automatic behavior analysis
US7991837 * 24 nov 2010 2 aug 2011 Cme Advantage, Inc. Systems and methods for networked, in-context, high resolution image viewing
US7995078 * 28 sept 2005 9 aug 2011 Noregin Assets, N.V., L.L.C. Compound lenses for multi-source data presentation
US8009863 * 30 juni 2008 30 aug 2011 Videomining Corporation Method and system for analyzing shopping behavior using multiple sensor tracking
US8107699 * 10 juli 2008 31 jan 2012 Siemens Medical Solutions Usa, Inc. Feature processing for lung nodules in computer assisted diagnosis
US8145007 * 15 april 2008 27 maart 2012 Grandeye, Ltd. Image processing of regions in a wide angle video camera
US8254679 * 13 okt 2008 28 aug 2012 Xerox Corporation Content-based image harmonization
US8274552 * 25 mei 2011 25 sept 2012 3Dmedia Corporation Primary and auxiliary image capture devices for image processing and related methods
US8275175 * 29 juli 2005 25 sept 2012 Telecom Italia S.P.A. Automatic biometric identification based on face recognition and support vector machines
US8280136 * 18 sept 2006 2 okt 2012 The Ohio State University Method and apparatus for detecting intraventricular dyssynchrony
US8306265 * 18 maart 2009 6 nov 2012 Eastman Kodak Company Detection of animate or inanimate objects
US8311915 * 14 okt 2009 13 nov 2012 Noregin Assets, N.V., LLC Detail-in-context lenses for interacting with objects in digital image presentations
US20010012379 * 11 april 2001 9 aug 2001 Hirokazu Amemiya Mobile object combination detection apparatus and method
US20020008785 * 11 april 2001 24 jan 2002 Noriyuki Yamaguchi Motion detecting apparatus
US20020009719 * 5 nov 1998 24 jan 2002 David R. Walt Fiber optic biosensor for selectively detecting oligonucleotide species in a mixed fluid sample
US20020027616 * 19 juli 2001 7 maart 2002 Lg Electronics Inc. Wipe and special effect detection method for MPEG-compressed video using spatio-temporal distribution of macro blocks
US20020028003 * 27 maart 2001 7 maart 2002 Krebs David E. Methods and systems for distinguishing individuals utilizing anatomy and gait parameters
US20020037103 * 26 dec 2000 28 maart 2002 Hong Qi He Method of and apparatus for segmenting a pixellated image
US20020039387 * 17 okt 2001 4 april 2002 Eric Auvray Adaptive method of encoding and decoding a series of pictures by transformation, and devices for implementing this method
US20020094135 * 10 mei 2001 18 juli 2002 Yeda Research And Development Co., Limited Apparatus and method for spatio-temporal alignment of image sequences
US20020110195 * 8 feb 2002 15 aug 2002 Eric Auvray Adaptive method of encoding and decoding a series of pictures by transformation, and devices for implementing this method
US20020142477 * 24 april 2001 3 okt 2002 Lewis Nathan S. Spatiotemporal and geometric optimization of sensor arrays for detecting analytes fluids
US20020159536 * 3 mei 2002 31 okt 2002 At&T Corp. Multi-channel parallel/serial concatenated convolutional codes and trellis coded modulation encoder/decoder
US20020164084 * 1 mei 2001 7 nov 2002 Scott Baggs System and method for improving image quality in processed images
US20030028005 * 11 aug 1999 6 feb 2003 J. Fernando Bazan Mammalian proteins; related reagents and methods
US20030088532 * 20 aug 2002 8 mei 2003 Hampshire John B. Method and apparatus for learning to classify patterns and assess the value of decisions
US20030108873 * 28 aug 2001 12 juni 2003 Dahlberg James E. Systems for the detection of target sequences
US20030185420 * 28 maart 2003 2 okt 2003 Jason Sefcik Target detection method and system
US20040014143 * 29 mei 2003 22 jan 2004 Haskins William E. Method and apparatus for detecting and monitoring peptides, and peptides identified therewith
US20040093166 * 15 sept 2003 13 mei 2004 Kil David H. Interactive and automated tissue image analysis with global training database and variable-abstraction processing in cytological specimen classification and laser capture microdissection applications
US20040115683 * 28 jan 2002 17 juni 2004 Medford June Iris Analysis of gene expression and biological function using optical imaging
US20040190092 * 2 april 2004 30 sept 2004 Kia Silverbrook Scanning device for coded data
US20040194129 * 31 maart 2003 30 sept 2004 Carlbom Ingrid Birgitta Method and apparatus for intelligent and automatic sensor control using multimedia database system
US20040233987 * 21 mei 2003 25 nov 2004 Porikli Fatih M. Method for segmenting 3D objects from compressed videos
US20040249848 * 6 juni 2003 9 dec 2004 Carlbom Ingrid Birgitta Method and apparatus for intelligent and automatic alert management using multimedia database system
US20050002572 * 1 juli 2004 6 jan 2005 General Electric Company Methods and systems for detecting objects of interest in spatio-temporal signals
US20050042230 * 9 juli 2004 24 feb 2005 Id Biomedical Corporation Of Quebec Subunit vaccine against respiratory syncytial virus infection
US20050048527 * 20 feb 2004 3 maart 2005 Third Wave Technologies, Inc. Endonuclease-substrate complexes
US20050104958 * 13 nov 2003 19 mei 2005 Geoffrey Egnal Active camera video-based surveillance systems and methods
US20050134685 * 22 dec 2003 23 juni 2005 Objectvideo, Inc. Master-slave automated video-based surveillance system
US20050165789 * 22 dec 2004 28 juli 2005 Minton Steven N. Client-centric information extraction system for an information network
US20050166163 * 23 jan 2004 28 juli 2005 Chang Nelson L.A. Systems and methods of interfacing with a machine
US20050203927 * 3 maart 2005 15 sept 2005 Vivcom, Inc. Fast metadata generation and delivery
US20050226502 * 31 maart 2004 13 okt 2005 Microsoft Corporation Stylization of video
US20060045346 * 13 mei 2005 2 maart 2006 Hui Zhou Method and apparatus for locating and extracting captions in a digital image
US20060050958 * 19 juli 2005 9 maart 2006 Kazunori Okada System and method for volumetric tumor segmentation using joint space-intensity likelihood ratio test
US20060056056 * 19 juli 2005 16 maart 2006 Grandeye Ltd. Automatically expanding the zoom capability of a wide-angle video camera
US20060101060 * 7 sept 2005 11 mei 2006 Kai Li Similarity search system with compact data structures
US20060176209 * 10 feb 2005 10 aug 2006 Raytheon Company Recognition algorithm for the unknown target rejection based on shape statistics obtained from orthogonal distance function
US20060221181 * 30 maart 2006 5 okt 2006 Cernium, Inc. Video ghost detection by outline
US20060222206 * 30 maart 2006 5 okt 2006 Cernium, Inc. Intelligent video behavior recognition with multiple masks and configurable logic inference module
US20060262184 * 2 nov 2005 23 nov 2006 Yissum Research Development Company Of The Hebrew University Of Jerusalem Method and system for spatio-temporal video warping
US20060282236 * 12 aug 2003 14 dec 2006 Axel Wistmuller Method, data processing device and computer program product for processing data
US20060291695 * 24 juni 2005 28 dec 2006 Objectvideo, Inc. Target detection and tracking from overhead video streams
US20070052803 * 8 sept 2005 8 maart 2007 Objectvideo, Inc. Scanning camera-based video surveillance system
US20070058717 * 9 sept 2005 15 maart 2007 Objectvideo, Inc. Enhanced processing for scanning video
US20070116347 * 9 okt 2006 24 mei 2007 Siemens Corporate Research Inc Devices, Systems, and Methods for Improving Image Consistency
US20070160274 * 10 jan 2006 12 juli 2007 Adi Mashiach System and method for segmenting structures in a series of images
US20070165936 * 22 juni 2005 19 juli 2007 Glory Ltd. Image checking device, image checking method, and image checking program
US20070230781 * 21 sept 2006 4 okt 2007 Koji Yamamoto Moving image division apparatus, caption extraction apparatus, method and program
US20070231791 * 10 mei 2004 4 okt 2007 Olsen Nancy J Gene Equation to Diagnose Rheumatoid Arthritis
US20070263915 * 18 juli 2007 15 nov 2007 Adi Mashiach System and method for segmenting structures in a series of images
US20070269078 * 27 juli 2007 22 nov 2007 Lockheed Martin Corporation Two dimension autonomous isotropic detection technique
US20080131920 * 4 okt 2006 5 juni 2008 The Forsyth Institute Ion flux in biological processes, and methods related thereto
US20080152192 * 10 dec 2007 26 juni 2008 Ingenious Targeting Laboratory, Inc. System For 3D Monitoring And Analysis Of Motion Behavior Of Targets
US20080195284 * 1 dec 2004 14 aug 2008 Zorg Industries Pty Ltd Level 2 Integrated Vehicular System for Low Speed Collision Avoidance
US20080201101 * 13 maart 2006 21 aug 2008 Creaform Inc. Auto-Referenced System and Apparatus for Three-Dimensional Scanning
US20080233595 * 9 aug 2007 25 sept 2008 Prince Henry's Institute Of Medical Research Method for diagnosing intertility
US20080298674 * 27 mei 2008 4 dec 2008 Image Masters Inc. Stereoscopic Panoramic imaging system
US20080320403 * 25 juni 2007 25 dec 2008 Nechemia Glaberson Accessing data using its chart symbols
US20090024547 * 17 juli 2007 22 jan 2009 Ut-Battelle, Llc Multi-intelligent system for toxicogenomic applications (mista)
US20090040301 * 6 aug 2007 12 feb 2009 Sandler Michael S Digital pan, tilt and zoom
US20090041328 * 10 juli 2008 12 feb 2009 Siemens Corporate Research, Inc. Feature Processing For Lung Nodules In Computer Assisted Diagnosis
US20090073265 * 13 april 2007 19 maart 2009 Curtin University Of Technology Virtual observer
US20090185078 * 17 jan 2008 23 juli 2009 Van Beek Petrus J L Systems and methods for video processing based on motion-aligned spatio-temporal steering kernel regression
US20090232353 * 9 nov 2007 17 sept 2009 University Of Maryland Method and system for markerless motion capture using multiple cameras
US20090285469 * 3 juni 2009 19 nov 2009 The Regents Of The University Of California Visual-servoing optical microscopy
US20090310444 * 8 juni 2009 17 dec 2009 Atsuo Hiroe Signal Processing Apparatus, Signal Processing Method, and Program
US20090310883 * 10 juni 2009 17 dec 2009 Fujifilm Corporation Image processing apparatus, method, and program
US20090316990 * 19 juni 2009 24 dec 2009 Akira Nakamura Object recognition device, object recognition method, program for object recognition method, and recording medium having recorded thereon program for object recognition method
US20100021009 * 24 juli 2009 28 jan 2010 Wei Yao Method for moving targets tracking and number counting
US20100021378 * 19 juli 2007 28 jan 2010 Spectrum Dynamics Llc Imaging protocols
US20100033574 * 5 dec 2006 11 feb 2010 Yang Ran Method and System for Object Surveillance and Real Time Activity Recognition
US20100070483 * 13 juli 2009 18 maart 2010 Lior Delgo Apparatus and software system for and method of performing a visual-relevance-rank subsequent search
US20100070523 * 13 juli 2009 18 maart 2010 Lior Delgo Apparatus and software system for and method of performing a visual-relevance-rank subsequent search
US20100092085 * 13 okt 2008 15 april 2010 Xerox Corporation Content-based image harmonization
US20100097526 * 14 feb 2008 22 april 2010 Photint Venture Group Inc. Banana codec
US20100098289 * 9 juli 2009 22 april 2010 Florida Atlantic University System and method for analysis of spatio-temporal data
US20100100001 * 23 dec 2009 22 april 2010 Teledyne Scientific & Imaging, Llc Fixation-locked measurement of brain responses to stimuli
US20100104135 * 23 jan 2008 29 april 2010 Nec Corporation Marker generating and marker detecting system, method and program
US20100111374 * 3 aug 2009 6 mei 2010 Adrian Stoica Method for using information in human shadows and their dynamics
US20100202662 * 12 feb 2009 12 aug 2010 Laser Technology, Inc. Vehicle classification by image processing with laser range finder
US20100245157 * 25 aug 2009 30 sept 2010 Wicks Michael C Generalized inner product method and apparatus for improved detection and discrimination
US20100245582 * 8 juni 2009 30 sept 2010 Syclipse Technologies, Inc. System and method of remote surveillance and applications therefor
US20100245583 * 8 juni 2009 30 sept 2010 Syclipse Technologies, Inc. Apparatus for remote surveillance and applications therefor
US20100245670 * 30 maart 2009 30 sept 2010 Sharp Laboratories Of America, Inc. Systems and methods for adaptive spatio-temporal filtering for image and video upscaling, denoising and sharpening
US20100262374 * 21 mei 2007 14 okt 2010 Jenq-Neng Hwang Systems and methods for analyzing nanoreporters
US20110002517 * 2 maart 2009 6 jan 2011 Koninklijke Philips Electronics N.V. Method for analyzing a tube system
US20110065072 * 16 sept 2009 17 maart 2011 Duffy Charles J Method and system for quantitative assessment of word recognition sensitivity
US20110142335 * 11 dec 2009 16 juni 2011 Bernard Ghanem Image Comparison System and Method
US20110157355 * 28 dec 2009 30 juni 2011 Yuri Ivanov Method and System for Detecting Events in Environments
US20110317009 * 6 sept 2010 29 dec 2011 MindTree Limited Capturing Events Of Interest By Spatio-temporal Video Analysis
US20120002047 * 24 mei 2011 5 jan 2012 Kwang Ho An Monitoring camera and method of tracing sound source
US20120038776 * 17 juni 2011 16 feb 2012 Grandeye, Ltd. Automatically Expanding the Zoom Capability of a Wide-Angle Video Camera
US20120092494 * 23 sept 2011 19 april 2012 Cernium Corporation Directed attention digital video recordation
US20120114245 * 9 nov 2011 10 mei 2012 Tata Consultancy Services Limited Online Script Independent Recognition of Handwritten Sub-Word Units and Words
US20120128254 * 22 juli 2010 24 mei 2012 Nec Corporation Marker generation device, marker generation detection system, marker generation detection device, marker, marker generation method, and program therefor
US20120206606 * 23 april 2012 16 aug 2012 Joseph Robert Marchese Digital video system using networked cameras
EP1845477A2 19 feb 2007 17 okt 2007 Kabushiki Kaisha Toshiba Moving image division apparatus, caption extraction apparatus, method and program
1 Aissa Saoudi, Hassane Essafi ,"Spatio-Temporal Video Slice Edges Analysis for Shot Transition Detection and Classification", Waset, vol. 4 No. 3 Summer 2008.
2 C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, "Pfinder: Real-Time Tracking of the Human Body," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, No. 7, pp. 780-785, Jul. 1997.
3 K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wallflower: Principles and Practice of Background Maintenance," in International Conference on Computer Vision, Sep. 1999.
4 Niyogi, S.A., Adelson, E.H., "Analyzing and Recognizing Walking Figures in XYT", CVPR94(469-474), CVPR 1994 IEEE Computer Society Conference Date: Jun. 21-23, 1994.
US9063219 * 4 okt 2012 23 juni 2015 Pixart Imaging Inc. Optical touch system
US9459351 13 mei 2015 4 okt 2016 Pixart Imaging Inc. Image system
US20130088576 * 4 okt 2012 11 april 2013 Pixart Imaging Inc. Optical touch system
Classificatie in de VS 348/575, 382/224, 348/607
Internationale classificatie H04N5/213
Coöperatieve classificatie G06K9/00744
6 sept 2010 AS Assignment
Owner name: MINDTREE LIMITED, INDIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KUMARASWAMY, SURESH KIRTHI;CHANDRASHEKAR, PUNEETH BILAGUNDA;SANGAPPA, HEMANTH KUMAR;AND OTHERS;REEL/FRAME:024941/0290
27 sept 2017 MAFP