Composite behavior analysis for video surveillance using hierarchical dynamic Bayesian networks

Details Composite behavior analysis for video surveillance using hierarchical dynamic Bayesian networks Composite behavior analysis for video surveillance using hierarchical dynamic Bayesian networks

Mar 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011opten..50c7201c&link_type=abstract

Optical Engineering, Volume 50, Issue 3, pp. 037201-037201-11 (2011).

Computer Science

Belief Networks, Object Recognition, Video Surveillance, Pattern Recognition, Telecommunications: Signal Transmission And Processing, Communication Satellites

Scientific paper

Analyzing composite behaviors involving objects from multiple categories in surveillance videos is a challenging task due to the complicated relationships among human and objects. This paper presents a novel behavior analysis framework using a hierarchical dynamic Bayesian network (DBN) for video surveillance systems. The model is built for extracting objects' behaviors and their relationships by representing behaviors using spatial-temporal characteristics. The recognition of object behaviors is processed by the DBN at multiple levels: features of objects at low level, objects and their relationships at middle level, and event at high level, where event refers to behaviors of a single type object as well as behaviors consisting of several types of objects such as ``a person getting in a car.'' Furthermore, to reduce the complexity, a simple model selection criterion is addressed, by which the appropriated model is picked out from a pool of candidate models. Experiments are shown to demonstrate that the proposed framework could efficiently recognize and semantically describe composite object and human activities in surveillance videos.

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