Human-Body Action Recognition Based on Dense Trajectories and Video Saliency

The traditional dense trajectory algorithm has achieved great success in human-body action recognition. However, the trajectories of the action and background motions are processed equally during algorithm's formation, which leads to redundant video representation and limited recognition accuracy. In this paper, the patterns of the background and behavioral motions are compared, a sparse error matrix is obtained using low-rank matrix decomposition on the basis of the sparse coefficient matrix of the feature dictionary, and a saliency map is solved. The saliency map is then used as the base for representing human-body action in only the action-related areas. The validity of this method is confirmed based on the open datasets UCF Sports and YouTube.