Spatio-temporal GCN with Geometric Features Fusion for Action Recognition
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Recently, the research on skeleton-based action recognition has attracted a lot of attention. As the graph convolutional networks can better model the internal dependencies of non-regular data, the spatio-temporal graph convolutional network (ST-GCN) has become the preferred network framework in this field. However, most of the current improvement methods based on the ST-GCN framework ignore the geometric features contained in the skeleton sequences. In this study, we exploit the geometric features of the skeleton joint as the feature enhancement of the ST-GCN framework, which has the advantage of visual invariance without additional parameters. Further, we integrate the geometric feature of the skeleton joint with earlier features to develop ST-GCN with geometric features. Finally, the experimental results show that the proposed framework achieves higher accuracy on both NTU-RGB+D dataset and NTU-RGB+D 120 dataset than other action recognition models such as ST-GCN, 2s-AGCN, and SGN.

    Reference
    Related
    Cited by
Get Citation

邹浩立.基于融合几何特征时空图卷积网络的动作识别.计算机系统应用,2022,31(10):261-269

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:January 11,2022
  • Revised:January 30,2022
  • Adopted:
  • Online: June 28,2022
  • Published:
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063