View-independent human action recognition by action hypersphere in nonlinear subspace

Though recognizing human action from video is important to applications like visual surveillance, some hurdles still slower the progress of action recognition. One of the main difficulties is view dependency, and this causes the degeneration of many recognition algorithms. In this paper, we propose a template-based view-independent human action recognition approach. The action template comprises a series of "action hyperspheres" in a nonlinear subspace and encodes multi-view information of several typical human actions to facilitate the view-independent recognition. Given an input action from video, we first compute the Motion History Image (MHI) and corresponding polar feature according to the extracted human silhouettes; recognition is achieved by evaluating the distances between the embedding of the polar feature and the virtual centers of the hyperspheres. Experiments show that our approach maintains high recognition accuracy in free viewpoints, and is more computationally efficient compared with classical kNN approach.