Violence classification based on shape variations from multiple views

Most existing algorithms for human behavior analysis concentrate on action recognition through assuming that input sequences are well pre-segmented and restricting examples into a small vocabulary. In this paper, we present a novel action violence classification framework which directly evaluates the potential threat based on shape variations. We extract silhouettes as input features, employ the R transform to project binary shapes into the Radon space, and fuse multiple views to classify action violence. Experimental results on the INRIA IXMAS database demonstrate the efficiency and robustness of the proposed method.