Apparatus and methods for the detection of abnormal motion in a video stream
First Claim
Patent Images
1. An apparatus for detection of abnormal activity in a video stream, the video stream having video frames, the apparatus comprising:
- a system maintenance and setup module;
a module that extracts motion vectors, from each of the video frames, that represent an approximate common movement direction of a predetermined sub-part of each of said frames;
a system training module that creates at least one statistical model representing usual activity based on said motion vectors or on at least one motion feature generated from said motion vectors; and
a motion detection module that detects abnormal activity by determining according to the at least one statistical model, that a probability of at least one motion vector of said motion vectors associated with said predetermined sub-part or said at least one motion feature extracted from the at least one motion vector associated with said predetermined sub-part, is below a predetermined threshold when compared to the said at least one statistical model,wherein said predetermined sub-part is a macro-block; and
wherein the at least one statistical model is represented as a one-dimensional histogram representing the distribution of values of one of the at least one motion feature.
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Abstract
An apparatus and method for detection of abnormal motion in video stream, having a training phase for defining normal motion and a detection phase for detecting abnormal motions in the video stream is provided. Motion is detected according to motion vectors and motion features extracted from video frames.
97 Citations
20 Claims
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1. An apparatus for detection of abnormal activity in a video stream, the video stream having video frames, the apparatus comprising:
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a system maintenance and setup module; a module that extracts motion vectors, from each of the video frames, that represent an approximate common movement direction of a predetermined sub-part of each of said frames; a system training module that creates at least one statistical model representing usual activity based on said motion vectors or on at least one motion feature generated from said motion vectors; and a motion detection module that detects abnormal activity by determining according to the at least one statistical model, that a probability of at least one motion vector of said motion vectors associated with said predetermined sub-part or said at least one motion feature extracted from the at least one motion vector associated with said predetermined sub-part, is below a predetermined threshold when compared to the said at least one statistical model, wherein said predetermined sub-part is a macro-block; and wherein the at least one statistical model is represented as a one-dimensional histogram representing the distribution of values of one of the at least one motion feature. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. An apparatus for detection of abnormal activity in a video stream, the video stream having video frames, the apparatus comprising:
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a system maintenance and setup module; a module that extracts motion vectors, from each of the video frames, that represent an approximate common movement direction of a predetermined sub-part of each of said frames; a system training module that creates at least one statistical model representing usual activity based on said motion vectors or on at least one motion feature generated from said motion vectors; and a motion detection module that detects abnormal activity by determining according to the at least one statistical model, that a probability of at least one motion vector of said motion vectors associated with said predetermined sub-part or said at least one motion feature extracted from the at least one motion vector associated with said predetermined sub-part, is below a predetermined threshold when compared to the said at least one statistical model, wherein said predetermined sub-part is a macro-block; and wherein the at least one statistical model is represented as a multi-dimensional histogram, wherein each dimension of the multi-dimensional histogram represents a distribution of values of one of the at least one motion feature.
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10. An apparatus for detection of abnormal activity in a video stream, the video stream having video frames, the apparatus comprising:
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a system maintenance and setup module; a module that extracts motion vectors, from each of the video frames, that represent an approximate common movement direction of a predetermined sub-part of each of said frames; a system training module that creates at least one statistical model representing usual activity based on said motion vectors or on at least one motion feature generated from said motion vectors; and a motion detection module that detects abnormal activity by determining according to the at least one statistical model, that a probability of at least one motion vector of said motion vectors associated with said predetermined sub-part or said at least one motion feature extracted from the at least one motion vector associated with said predetermined sub-part, is below a predetermined threshold when compared to the said at least one statistical model, wherein said predetermined sub-part is a macro-block; and wherein the statistical model representing the distribution of an at least one motion feature is created using a k-means method.
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11. A method for detection of abnormal activity in a video stream, the video stream having video frames, the method comprising:
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capturing frames with a video camera; extracting motion vectors, from each of the frames, that represent an approximate common movement direction of a predetermined sub-part of said frames; creating at least one statistical model, representing usual activity, based on said motion vectors, or on an at least one motion feature generated from said motion vectors; detecting abnormal activity by determining according to the at least one statistical model that a probability of at least one motion vector associated with the said predetermined sub-part or at least one motion feature extracted from the at least one motion vector associated with said predetermined sub-part, is below a predetermined threshold; and relaying a warning indication for the abnormal activity to a warning device, wherein the sub-part is a macro-block; and wherein the at least one statistical model is a one-dimensional histogram representing the distribution of values of the at least one motion feature. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A method for detection of abnormal activity in a video stream, the video stream having video frames, the method comprising:
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capturing frames with a video camera; extracting motion vectors, from each of the frames, that represent an approximate common movement direction of a predetermined sub-part of said frames; creating at least one statistical model, representing usual activity, based on said motion vectors, or on an at least one motion feature generated from said motion vectors; detecting abnormal activity by determining according to the at least one statistical model that a probability of at least one motion vector associated with the said predetermined sub-part or at least one motion feature extracted from the at least one motion vector associated with said predetermined sub-part, is below a predetermined threshold; and relaying a warning indication for the abnormal activity to a warning device, wherein the sub-part is a macro-block; and wherein the at least one statistical model is a multi-dimensional histogram, wherein each dimension of the multi-dimensional histogram represents a distribution of values of one of the at least one motion feature.
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20. A method for detection of abnormal activity in a video stream, the video stream having video frames, the method comprising:
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capturing frames with a video camera; extracting motion vectors, from each of the frames, that represent an approximate common movement direction of a predetermined sub-part of said frames; creating at least one statistical model, representing usual activity, based on said motion vectors, or on an at least one motion feature generated from said motion vectors; detecting abnormal activity by determining according to the at least one statistical model that a probability of at least one motion vector associated with the said predetermined sub-part or at least one motion feature extracted from the at least one motion vector associated with said predetermined sub-part, is below a predetermined threshold; and relaying a warning indication for the abnormal activity to a warning device, wherein the sub-part is a macro-block; and wherein the statistical model representing the distribution of an at least one motion feature is created using a k-means method.
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Specification