Multi-Level Graph Convolutional Networks with Enhanced Partitioning Strategies for Skeleton-Based Action Recognition

The methods of skeleton-based human action recognition (HAR) have gained widespread interest due to their robustness in capturing actions amidst changing environments and intricate backgrounds. Utilizing graph convolutional networks (GCNs) to describe the human skeleton for HAR has been shown to achieve impressive performance. However, most GCN-based methods only consider the relationships between adjacent joints, overlooking the relationships between joints that are not connected by natural physical links. Therefore, we propose a novel method called Multi-level Graph Convolutional Network, abbreviated as ML-GCN. We have refined the original partitioning strategy, introducing three novel strategies to more effectively enhance the connectivity among distant joints. Additionally, we incorporate a multi-level graph convolutional network and a non-local temporal convolutional network to better extract spatio-temporal features. Experiments conducted on the NTU-RGB+D and Kinetics datasets demonstrate that our model achieves a certain improvement in accuracy.