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Human activity recognition in multiview video

In this paper, a novel multiview video based human activity recognition system which automatic detects of such behavior as fainting, a fight or a call for help is presented. The approach proposed in this paper used a directed graphical model based on propagation nets, a subset of dynamic Bayesian networks approaches, to model the behaviors. The performance of activity recognition is analyzed for three methods of characteristic points forming a behavior descriptor (four extreme points over contour, four extreme points over contour with different normalization process and n-evenly distributed points on the contour). The results prove high score of recognition of the system for “Calling for help”, “Faint”, “Fight”, “Falling” and “Bend at the waist” behaviors.