ACTION RECOGNITION IN STILL IMAGES USING A COMBINATION OF HUMAN POSE AND CONTEXT INFORMATION

In this work, a novel method is proposed for recognizing human actions in still images, which incorporates both pose and context information. Poselet-based action classifiers are learned using Poselet Activation Vector as features, which contain pose information for each action. And context-based action classifiers for each action are learned on contextual information, which is obtained by sparse coding on foreground and background. The confidences of an image belonging to each action are obtained through summing up the probability outputs of the poselet-based and the context-based classifiers. The contribution of this work is three folded. Firstly, sparse coding is adopted to find compact patterns of the original features. Secondly, a block coordinate descent algorithm is proposed for sparse coding, which can be performed very fast in practice. Thirdly, both pose and context information are taken into consideration for action recognition. The experimental results show the proposed method achieves the state-of-the-art performance on several benchmarks.