Pixel Convolutional Networks for Skeleton-Based Human Action Recognition

Human action recognition is an important field in computer vision. Skeleton-based models of human obtain more attention in related researches because of strong robustness to external interference factors. In traditional researches the form of the feature is usually so hand-crafted that effective feature is difficult to extract from skeletons. In this paper a unique method is proposed for human action recognition called Pixel Convolutional Networks, which use a natural and intuitive way to extract skeleton feature from two dimensions, space and time. It achieves good performance compared with mainstream methods in the past few years in the large dataset NTU-RGB+D.