EFFICIENT HUMAN ACTION DETECTION: A COARSE-TO-FINE STRATEGY

This paper proposes a coarse-to-fine strategy to detect human actions in the realistic videos given a single example of such action. The proposed method is learning-free and doesn't require any prior knowledge. Input video is separated into a batch of spatio-temporal volumes based on chi-square distance measure of the volumetric features and further identified by contextual motion information. Instead of the exhaustive search, query action is localized by matching local salient geometric features only between itself and the pruned spatio-temporal volumes. The competitive results obtained from the evaluation on a collection of challenging action data indicate the effectiveness and the computational efficiency of our method.