Human action recognition based on tensor shape descriptor
Human action recognition is an important task. This study presents an efficient framework for recognising action with a 3D skeleton kinematic joint model in less computational time for practical usage. First, a tensor shape descriptor (TSD) is proposed in this study, which takes advantage of the spatial independence of body joints, avoids a lot of difficult problem of the explicit motion estimation required in traditional methods, reserves the spatial information of each frame. Thus, the new TSD is a complete and view-invariant descriptor. Second, a novel tensor dynamic time warping (TDTW) method is proposed to measure joint-to-joint similarity of 3D skeletal body joints locally in the temporal extent, which is implemented by extending DTW to that of two multiway data arrays (or tensors). Then, a multi-linear projection process is employed to map the TSD to a low-dimensional tensor subspace, which is classified by the nearest neighbour classifier. The experiment results on the public action data set (MSR-Action3D) and motion capture data set (CMU_Mocap) show that the proposed method can achieve a comparable or better performance in recognition accuracy compared with the state-of-the-art approaches.