ANOMALOUS OBJECT INTERACTION DETECTION AND REPORTING
First Claim
1. A computer-implemented method for analyzing a scene depicted in an input stream of video frames captured by a video camera, the method comprising:
- receiving two or more sequences, wherein each sequence corresponds to a segment of a trajectory taken by a respective object through the scene;
determining, via one or more processors, whether the objects interact based on a spatio-temporal proximity of the objects; and
if the objects interact;
mapping each sequence of the two or more sequences to a sequence cluster, anddetermining a rareness value for the sequence clusters to which the sequences map based on a learned joint probability of the sequence clusters.
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Abstract
Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.
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Citations
28 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, the method comprising:
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receiving two or more sequences, wherein each sequence corresponds to a segment of a trajectory taken by a respective object through the scene; determining, via one or more processors, whether the objects interact based on a spatio-temporal proximity of the objects; and if the objects interact; mapping each sequence of the two or more sequences to a sequence cluster, and determining a rareness value for the sequence clusters to which the sequences map based on a learned joint probability of the sequence clusters. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A non-transitory computer-readable storage medium storing instructions, which when executed by a computer system, perform operations for analyzing a scene depicted in an input stream of video frames captured by a video camera, the operations comprising:
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receiving two or more sequences, wherein each sequence corresponds to a segment of a trajectory taken by a respective object through the scene; determining, via one or more processors, whether the objects interact based on a spatio-temporal proximity of the objects; and if the objects interact; mapping each sequence of the two or more sequences to a sequence cluster, and determining a rareness value for the sequence clusters to which the sequences map based on a learned joint probability of the sequence clusters. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A system, comprising:
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a processor; and a memory, wherein the memory includes an application program configured to perform operations for analyzing a scene depicted in an input stream of video frames captured by a video camera, the operations comprising; receiving two or more sequences, wherein each sequence corresponds to a segment of a trajectory taken by a respective object through the scene, determining, via one or more processors, whether the objects interact based on a spatio-temporal proximity of the objects, and if the objects interact; mapping each sequence of the two or more sequences to a sequence cluster; and determining a rareness value for the sequence clusters to which the sequences map based on a learned joint probability of the sequence clusters. - View Dependent Claims (24, 25, 26, 27, 28)
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Specification