Multi-heads Attention Graph Convolutional Networks for Skeleton-Based Action Recognition
Guowei Zhang, Xin Zhang · 2019
Compared with video-based action recognition, skeleton-based methods have more compact and accurate representation. In the recent development, human skeleton is modeled as graph and graph-based deep learning method is applied for recognition. Generally, in actions, few joints are pivotal other than the whole body, like waving hands mostly related with hand and arm joints. Hence, we propose the data-driven multi-head attention model for graph convolutional networks. The attention model identifies key joints of every action by introducing two regularization terms, i.e., spatial diversity and local continuity. Further, we introduce the joint-wise second order motion information as the additional feature on the graph node, which represents the motion variation explicitly. We have tested our methods on the largest popular dataset, NTU-RGB+D, and we reach state-of-the-art performance.