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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...
A model-based probabilistic method of human action recognition is presented in this paper. We employ supervised neighborhood preserving embedding (NPE) to preserve the underlying structure of articulated action space during dimensionality reduction. Generative recognition structures like Hidden Markov Models often have to make unrealistic assumptions on the conditional independence and can not accommodate...
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