Unsupervised Learning of Functional Categories in Video Scenes

Abstract

Existing methods for video scene analysis are primarily concerned with learning motion patterns or models for anomaly detection. We present a novel form of video scene analysis where scene element categories such as roads, parking areas, sidewalks and entrances, can be segmented and categorized based on the behaviors of moving objects in and around them. We view the problem from the perspective of categorical object recognition, and present an approach for unsupervised learning of functional scene element categories. Our approach identifies functional regions with similar behaviors in the same scene and/or across scenes, by clustering histograms based on a trajectory-level, behavioral codebook. Experiments are conducted on two outdoor webcam video scenes with low frame rates and poor quality. Unsupervised classification results are presented for each scene independently, and also jointly where models learned on one scene are applied to the other.

This material is based upon work supported by the Defense Advanced Research Projects Agency under prime contract HR0011-06-C-0069, subcontract 070861. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA.

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