Continuously evolvable Bayesian Nets for human action analysis in videos

This paper proposes a novel data driven continuously evolvable Bayesian net (BN) framework to analyze human actions in video. In unpredictable video streams, only a few generic causal relations and their interrelations together with the dynamic changes of these interrelations are used to probabilistically estimate relatively complex human activities. Based on the available evidences in streaming videos, the proposed BN can dynamically change the number of nodes in every frame and different relations for the same nodes in different frames. The performance of the proposed BN framework is shown for complex movie clips where actions like hand on head or waist, standing close, and holding hands take place among multiple individuals under changing pose conditions. The proposed BN can represent and recognize the human activities in a scalable manner.