METHOD AND SYSTEM FOR AUTOMATICALLY DETECTING ANOMALIES AT A TRAFFIC INTERSECTION
First Claim
1. A multi-stage method for detecting anomalies in video footage of vehicle traffic, said method comprising:
- deriving in an offline process a set of clusters of nominal vehicle paths and a set of clusters of nominal trajectories within said nominal vehicle paths;
selecting in an offline process a set of features within each nominal trajectory among said set of clusters of nominal trajectories and deriving a probability distribution for features indicative of nominal vehicle behavior within said nominal trajectories; and
detecting in three successive stages, anomalies in an input video sequence, said anomalies corresponding respectively to a path, a trajectory, and feature distributions.
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Accused Products
Abstract
A method, system and processor-readable medium for automatically detecting anomalies at a traffic intersection. A set of clusters of nominal vehicle paths and a set of clusters of nominal trajectories within the nominal vehicle paths can be derived in an offline process. A set of features within each nominal trajectory among the set of clusters of nominal trajectories can be selected. A probability distribution for features indicative of nominal vehicle behavior within the nominal trajectories can be derived. An input video sequence can be received and presence of the anomaly in the vehicle path, trajectories and features within the input video sequence can be detected utilizing the derived path clusters, trajectory clusters, and feature distributions.
19 Citations
20 Claims
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1. A multi-stage method for detecting anomalies in video footage of vehicle traffic, said method comprising:
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deriving in an offline process a set of clusters of nominal vehicle paths and a set of clusters of nominal trajectories within said nominal vehicle paths; selecting in an offline process a set of features within each nominal trajectory among said set of clusters of nominal trajectories and deriving a probability distribution for features indicative of nominal vehicle behavior within said nominal trajectories; and detecting in three successive stages, anomalies in an input video sequence, said anomalies corresponding respectively to a path, a trajectory, and feature distributions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A multi-stage system for detecting anomalies in video footage of vehicle traffic, said system comprising:
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a processor; a data bus coupled to said processor; and a computer-usable medium embodying computer code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for; deriving in an offline process a set of clusters of nominal vehicle paths and a set of clusters of nominal trajectories within said nominal vehicle paths; selecting in an offline process a set of features within each nominal trajectory among said set of clusters of nominal trajectories and deriving a probability distribution for features indicative of nominal vehicle behavior within said nominal trajectories; and detecting in three successive stages, anomalies in an input video sequence, said anomalies corresponding respectively to a path, a trajectory, and feature distributions. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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20. A processor-readable medium storing code representing instructions to cause a process to perform a multi-stage method for detecting anomalies in video footage of vehicle traffic, said code comprising code to:
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derive in an offline process a set of clusters of nominal vehicle paths and a set of clusters of nominal trajectories within said nominal vehicle paths; select in an offline process a set of features within each nominal trajectory among said set of clusters of nominal trajectories and deriving a probability distribution for features indicative of nominal vehicle behavior within said nominal trajectories; and detect in three successive stages, anomalies in an input video sequence, said anomalies corresponding respectively to a path, a trajectory, and feature distributions.
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Specification