Abstract: This paper presented an efficient action recognition method based on Hu moment invariant features．Firstly,the Hu moment invariants were refined to be new features that are translation,rotation and scale invariant．Then an action was characterized by a 13-dimensional feature vector consisting of both Hu moment features and action speed features．The Hu moment features represent the Zone shape of the action,and the action speed features exhibit certain motion characteristics．Finally,a support vector machine(SVM),which is trained using labeled action frames,was applied to classify test sample actions into different categories．The proposed method is performed on real-world videos and achieves acceptable recognition rates with desirable computational efficiencies.

Key words: Behavior recognition,Zone shape,Hu moment invariant,Motion feature