DETECTING ANOMALOUS TRAJECTORIES IN A VIDEO SURVEILLANCE SYSTEM

• US 20110052000A1
• Filed: 08/31/2009
• Published: 03/03/2011
• Est. Priority Date: 08/31/2009
• Status: Active Grant

• Current Assignee: Motorola Solutions, Inc.
• Original Assignee: Behavioral Recognition Systems Incorporated
• Inventors: COBB, WESLEY KENNETH, SEOW, MING-JUNG, XU, GANG
• Granted Patent: US 8,285,060 B2
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/30232 Surveillance
• CPC Class Codes: G06T 2207/30241 Trajectory
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

Techniques are disclosed for determining anomalous trajectories of objects tracked over a sequence of video frames. In one embodiment, a symbol trajectory may be derived from observing an object moving through a scene. The symbol trajectory represents semantic concepts extracted from the trajectory of the object. Whether the symbol trajectory is anomalous may be determined, based on previously observed symbol trajectories. A user may be alerted upon determining that the symbol trajectory is anomalous.

Claims

1. A computer-implemented method for analyzing a sequence of video frames depicting a scene captured by a video camera, the method comprising:

passing each of a plurality of kinematic data vectors derived from analyzing a foreground object detected in the sequence of video frames to a plurality of adaptive resonance theory (ART) networks, each ART network modeling a subset of kinematic data parsed from the kinematic data vectors;

identifying, in each ART network, a cluster to which each respective subset of kinematic data parsed from the plurality of kinematic data vectors is mapped;

generating a primitive trajectory based on the clusters identified in each of the ART networks; and

generating a symbol trajectory from the primitive trajectory.

2. The computer-implemented method of claim 1, wherein the primitive trajectory includes a sequence of labels assigned to the clusters in a respective ART network, wherein the sequence corresponds to an order in which the plurality of kinematic data vectors are passed to the respective ART network.

3. The computer-implemented method of claim 1, wherein the symbol trajectory is generated using latent semantic analysis (LSA) and singular value decomposition (SVD) applied to the primitive trajectory.

4. The computer-implemented method of claim 1, further comprising:

computing a probability of observing the symbol trajectory in the scene.

5. The computer-implemented method of claim 4, wherein the probability of observing the symbol trajectory in the scene is computed based on a plurality of symbol trajectories previously observed in the scene.

6. The computer-implemented method of claim 4, wherein the probability of observing the symbol trajectory in the scene is computed using at least one of a Markov model, a hidden Markov model, and probabilistic latent semantic analysis.

7. The computer-implemented method of claim 4, further comprising:

upon determining that a probability of observing the symbol trajectory falls below a threshold value, alerting a user upon of the video surveillance system of an occurrence of an anomalous trajectory.

8. A computer-readable storage medium containing a program which, when executed by a processor, performs an operation for analyzing a sequence of video frames depicting a scene captured by a video camera, the operation comprising:

passing each of a plurality of kinematic data vectors derived from analyzing a foreground object detected in the sequence of video frames to a plurality of adaptive resonance theory (ART) networks, each ART network modeling a subset of kinematic data parsed from the kinematic data vectors;

identifying, in each ART network, a cluster to which each respective subset of kinematic data parsed from the plurality of kinematic data vectors is mapped;

generating a primitive trajectory based on the clusters identified in each of the ART networks; and

generating a symbol trajectory from the primitive trajectory.

9. The computer-readable storage medium of claim 8, wherein the primitive trajectory includes a sequence of labels assigned to the clusters in a respective ART network, wherein the sequence corresponds to an order in which the plurality of kinematic data vectors are passed to the respective ART network.

10. The computer-readable storage medium of claim 8, wherein the symbol trajectory is generated using latent semantic analysis (LSA) and singular value decomposition (SVD) applied to the primitive trajectory.

11. The computer-readable storage medium of claim 8, wherein the operation further comprises:

computing a probability of observing the symbol trajectory in the scene.

12. The computer-readable storage medium of claim 11, wherein the probability of observing the symbol trajectory in the scene is computed based on a plurality of symbol trajectories previously observed in the scene.

13. The computer-readable storage medium of claim 11, wherein the probability of observing the symbol trajectory in the scene is computed using at least one of a Markov model, a hidden Markov model, and probabilistic latent semantic analysis.

14. The computer-readable storage medium of claim 11, wherein the operation further comprises:

upon determining that a probability of observing the symbol trajectory falls below a threshold value, alerting a user upon of the video surveillance system of an occurrence of an anomalous trajectory.

15. A system, comprising:

a video input source configured to provide a sequence of video frames, 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, as depicted by the sequence of video frames captured by the video input source, the operation comprising;

passing each of a plurality of kinematic data vectors derived from analyzing a foreground object detected in a sequence of video frames to a plurality of adaptive resonance theory (ART) networks, each ART network modeling a subset of kinematic data parsed from the kinematic data vectors,

identifying, in each ART network, a cluster to which each respective subset of kinematic data parsed from the plurality of kinematic data vectors is mapped,

generating a primitive trajectory based on the clusters identified in each of the ART networks, and

generating a symbol trajectory from the primitive trajectory.

16. The system of claim 15, wherein the primitive trajectory includes a sequence of labels assigned to the clusters in a respective ART network, wherein the sequence corresponds to an order in which the plurality of kinematic data vectors are passed to the respective ART network.

17. The system of claim 15, wherein the symbol trajectory is generated using latent semantic analysis (LSA) and singular value decomposition (SVD) applied to the primitive trajectory.

18. The system of claim 15, further comprising:

computing a probability of observing the symbol trajectory in the scene.

19. The system of claim 18, wherein the probability of observing the symbol trajectory in the scene is computed based on a plurality of symbol trajectories previously observed in the scene.

20. The system of claim 18, wherein the probability of observing the symbol trajectory in the scene is computed using at least one of a Markov model, a hidden Markov model, and probabilistic latent semantic analysis.

21. The system of claim 18, further comprising:

upon determining that a probability of observing the symbol trajectory falls below a threshold value, alerting a user upon of the video surveillance system of an occurrence of an anomalous trajectory.