A new approach of action recognition based on Motion Stable Shape (MSS) features

Action recognition is actually considered as one of the most challenging areas in computer vision domain. In this paper, we propose a new approach based on utilization of motion boundaries to generate Motion Stable Shape (MSS) features to describe human actions in videos. In fact, we have considered actions as a set of human poses. Temporal evolution of each human pose is modeled by a set of new MSS feature's. Motion stable shapes of considered poses are defined by specific regions located at the borders of movements. Our modelisation is composed of different steps. First, a volume of optical flow frames highlighting the principal motions in poses is substracted. Then, motion boundaries are computed from the previous optical flow frames. Finally, maximally Stable Extremal Regions (MSER) are applied to motion boundaries frames in order to obtain MSS features. To predict classes of different human actions, the MSS features are combined with a standard bag-of-words representation. To prove the efficiency of our developed model, we have performed a set of experiments on four datasets: Weizmann, KTH, UFC and Hollywood. Obtained experimental results show that the proposed approach significantly outperforms state-of-the-art methods.