1 March 2011 Composite behavior analysis for video surveillance using hierarchical dynamic Bayesian networks
Author Affiliations +
Optical Engineering, 50(3), 037201 (2011). doi:10.1117/1.3554372
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.
Huanhuan Cheng, Runsheng Wang, Yong Shan, "Composite behavior analysis for video surveillance using hierarchical dynamic Bayesian networks," Optical Engineering 50(3), 037201 (1 March 2011). https://doi.org/10.1117/1.3554372

Back to Top