Motion-Guided Graph Convolutional Network for Human Action Recognition

被引：0
作者：
Li, Jingjing [1 ]
Huang, Zhangjin [1 ,2 ]
Zou, Lu [1 ]
机构：
[1] School of Data Science, University of Science and Technology of China, Hefei
[2] School of Computer Science and Technology, University of Science and Technology of China, Hefei
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2024年 / 36卷 / 07期
关键词：
action recognition; graph convolution; human skeleton; motion-guided topology;
D O I：
10.3724/SP.J.1089.2024.19898
中图分类号：
学科分类号：
摘要：
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.
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页码：1077 / 1086
页数：9
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