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 mo-tion-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.
万方数据
维普
