Adaptive most joint selection and covariance descriptions for a robust skeleton-based human action recognition

In this paper, we propose two effective manners of utilizing skeleton data for human action recognition (HAR). The proposed method on one hand takes advantage of the skeleton data thanks to their robustness to human appearance change as well as the real-time performance. On the other hand, it avoids inherent drawbacks of the skeleton data such as noises, incorrect human skeleton estimation due to self-occlusion of human pose. To this end, in terms of feature designing, we propose to extract covariance descriptors from joint velocity and combine them with those of joint position. In terms of 3-D skeleton-based activity representation, we propose two schemes to select the most informative joints. The proposed method is evaluated on two benchmark datasets. On the MSRAction-3D dataset, the proposed method outperformed different hand-designed features-based methods. On the challenging dataset CMDFall, the proposed method significantly improves accuracy when compared with techniques based on recent neuronal networks. Finally, we investigate the robustness of the proposed method via a cross-dataset evaluation.