Intra-trajectory anomaly detection using adaptive voting experts in a video surveillance system
First Claim
1. A computer-implemented method for analyzing a scene depicted in an input stream of video frames captured by a video camera of a video surveillance system, the method comprising:
- receiving a first sequence storing an ordered string of labels, wherein each label in the string corresponds to a cluster in an adaptive resonance theory (ART) network, wherein the string of labels is generated by mapping kinematic data vectors generated for a foreground object detected in the input stream of video frames to nodes of a self-organizing map (SOM) and clustering the nodes of the SOM using the ART network;
identifying one or more segments in the first sequence, wherein each segment includes a subsequence of the ordered string of labels in the first sequence, wherein the subsequence in the first one of the segments is determined to include a label not present in the ngram trie;
determining a probability of observing each of the one or more segments relative to a probability distribution generated from an ngram trie, wherein the ngram trie is generated from a plurality of previously observed sequences, wherein each sequence of the plurality stores an ordered string of labels assigned to clusters in the ART network;
upon determining a first one of the segments has a probability of being observed below a specified threshold, issuing an alert to a user of the video surveillance system.
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Accused Products
Abstract
A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
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Citations
22 Claims
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1. A computer-implemented method for analyzing a scene depicted in an input stream of video frames captured by a video camera of a video surveillance system, the method comprising:
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receiving a first sequence storing an ordered string of labels, wherein each label in the string corresponds to a cluster in an adaptive resonance theory (ART) network, wherein the string of labels is generated by mapping kinematic data vectors generated for a foreground object detected in the input stream of video frames to nodes of a self-organizing map (SOM) and clustering the nodes of the SOM using the ART network; identifying one or more segments in the first sequence, wherein each segment includes a subsequence of the ordered string of labels in the first sequence, wherein the subsequence in the first one of the segments is determined to include a label not present in the ngram trie; determining a probability of observing each of the one or more segments relative to a probability distribution generated from an ngram trie, wherein the ngram trie is generated from a plurality of previously observed sequences, wherein each sequence of the plurality stores an ordered string of labels assigned to clusters in the ART network; upon determining a first one of the segments has a probability of being observed below a specified threshold, issuing an alert to a user of the video surveillance system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A non-transitory computer-readable storage medium containing a program, which when executed on a processor, performs an operation for analyzing a scene depicted in an input stream of video frames captured by a video camera of a video surveillance system, the operation comprising:
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receiving a first sequence storing an ordered string of labels, wherein each label in the string corresponds to a cluster in an adaptive resonance theory (ART) network, wherein the string of labels is generated by mapping kinematic data vectors generated for a foreground object detected in the input stream of video frames to nodes of a self-organizing map (SOM) and clustering the nodes of the SOM using the ART network; identifying one or more segments in the first sequence, wherein each segment includes a subsequence of the ordered string of labels in the first sequence, wherein the subsequence in the first one of the segments is determined to include a label not present in the ngram trie; determining a probability of observing each of the one or more segments relative to a probability distribution generated from an ngram trie, wherein the ngram trie is generated from a plurality of previously observed sequences, wherein each sequence of the plurality stores an ordered string of labels assigned to clusters in the ART network; upon determining a first one of the segments has a probability of being observed below a specified threshold, issuing an alert to a user of the video surveillance system. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A video surveillance system, comprising:
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a video input source configured to provide an input stream of video frames captured by a video camera, each depicting a scene; a processor; and a memory containing a program, which, when executed on the processor is configured to perform an operation for analyzing the scene depicted in the input stream of video frames, the operation comprising; receiving a first sequence storing an ordered string of labels, wherein each label in the string corresponds to a cluster in an adaptive resonance theory (ART) network, wherein the string of labels is generated by mapping kinematic data vectors generated for a foreground object detected in the input stream of video frames to nodes of a self-organizing map (SOM) and clustering the nodes of the SOM using the ART network, identifying one or more segments in the first sequence, wherein each segment includes a subsequence of the ordered string of labels in the first sequence, wherein the subsequence in the first one of the segments is determined to include a label not present in the ngram trie, determining a probability of observing each of the one or more segments relative to a probability distribution generated from an ngram trie, wherein the ngram trie is generated from a plurality of previously observed sequences, wherein each sequence of the plurality stores an ordered string of labels assigned to clusters in the ART network, upon determining a first one of the segments has a probability of being observed below a specified threshold, issuing an alert to a user of the video surveillance system. - View Dependent Claims (17, 18, 19, 20, 21, 22)
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Specification