Motion-Guided Graph Convolutional Network for Human Action Recognition

The current skeleton-based human action recognition methods cannot model the changes in the dependence between joints over time, and the interaction of cross space-time information. To solve these problems, a novel motion-guided graph convolutional network (M-GCN) is proposed. Firstly, the high-level motion features are extracted from the skeleton sequence. Secondly, the predefined graphs and the learnable graphs are optimized by the motion-dependent correlations on the time dimension. And the different joint dependencies, i.e., the motion-guided topologies, are captured along the time dimension. Thirdly, the motion-guided topologies are used for spatial graph convolutions, and motion information is fused into spatial graph convolutions to realize the interaction of spatial-temporal information. Finally, spatial-temporal graph convolutions are applied alternately to implement precise human action recognition. Compared with the graph convolution method such as MS-G3D on the dataset NTU-RGB+D and the dataset NTU-RGB+D 120, the results show that the accuracy of the proposed method on the cross subject and cross view of NTU-RGB+D is improved to 92.3% and 96.7%, respectively, and the accuracy on the cross subject and cross setup of NTU-RGB+D 120 is improved to 88.8% and 90.2%, respectively. © 2024 Institute of Computing Technology. All rights reserved.