Interacting activity recognition using hierarchical durational-state dynamic bayesian network

Authors:
Youtian Du;Feng Chen;Wenli Xu;Weidong Zhang
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China
Venue:
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Year:
2006

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Abstract

Activity recognition is one of the most challenging problems in the high-level computer vision field. In this paper, we present a novel approach to interacting activity recognition based on dynamic Bayesian network (DBN). In this approach the features representing the human activities are divided into two classes: global features and local features, which are on two different spatial scales. To model and recognize human interacting activities, we propose a hierarchical durational-state DBN model (HDS-DBN). HDS-DBN combines the global features with local ones organically and reveals structure of interacting activities well. The effectiveness of this approach is demonstrated by experiments.