Centrality Graph Convolutional Networks for Skeleton-based Action Recognition

The topological structure of skeleton data plays a significant role in human action recognition. Combining the topological structure with graph convolutional networks has achieved remarkable performance. In existing methods, modeling the topological structure of skeleton data only considered the connections between the joints and bones, and directly use physical information. However, there exists an unknown problem to investigate the key joints, bones and body parts in every human action. In this paper, we propose the centrality graph convolutional networks to uncover the overlooked topological information, and best take advantage of the information to distinguish key joints, bones, and body parts. A novel centrality graph convolutional network firstly highlights the effects of the key joints and bones to bring a definite improvement. Besides, the topological information of the skeleton sequence is explored and combined to further enhance the performance in a four-channel framework. Moreover, the reconstructed graph is implemented by the adaptive methods on the training process, which further yields improvements. Our model is validated by two large-scale datasets, NTU-RGB+D and Kinetics, and outperforms the state-of-the-art methods.