CLASSIFIER ANOMALIES FOR OBSERVED BEHAVIORS IN A VIDEO SURVEILLANCE SYSTEM

• US 20120224746A1
• Filed: 05/15/2012
• Published: 09/06/2012
• Est. Priority Date: 09/17/2009
• Status: Active Grant

• Current Assignee: Motorola Solutions, Inc.
• Original Assignee: Behavioral Recognition Systems Incorporated
• Inventors: FRIEDLANDER, David, COBB, Wesley Kenneth, SAITWAL, Kishor Adinath, SEOW, Ming-Jung, XU, Gang
• Granted Patent: US 8,494,222 B2
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69

Abstract

Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.

Claims

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

receiving a set of data inputs derived by a computer vision engine configured to analyze pixels depicting a plurality of foreground objects in the sequence of video frames;

modeling behavior of the foreground objects in the scene by passing the received sensory data inputs to a first cluster layer of a plurality of layers, wherein the plurality of layers alternate between cluster layers and sequence layers and wherein the cluster layers generate clusters of sequences and the sequence layers generate sequences of clusters;

evaluating one or more of the cluster layers to identify an occurrence of a behavioral anomaly based on an input to one of the cluster layers; and

publishing an alert message indicating the occurrence of the behavioral anomaly.