Attention module-based spatial-temporal graph convolutional networks for skeleton-based action recognition

Kong, Y. and Li, L. and Zhang, K. and Ni, Q. and Han, J. (2019) Attention module-based spatial-temporal graph convolutional networks for skeleton-based action recognition. Journal of Electronic Imaging, 28 (4). ISSN 1017-9909

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Abstract

Skeleton-based action recognition is a significant direction of human action recognition, because the skeleton contains important information for recognizing action. The spatial-temporal graph convolutional networks (ST-GCN) automatically learn both the temporal and spatial features from the skeleton data and achieve remarkable performance for skeleton-based action recognition. However, ST-GCN just learns local information on a certain neighborhood but does not capture the correlation information between all joints (i.e., global information). Therefore, we need to introduce global information into the ST-GCN. We propose a model of dynamic skeletons called attention module-based-ST-GCN, which solves these problems by adding attention module. The attention module can capture some global information, which brings stronger expressive power and generalization capability. Experimental results on two large-scale datasets, Kinetics and NTU-RGB+D, demonstrate that our model achieves significant improvements over previous representative methods. © 2019 SPIE and IS&T.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Electronic Imaging
Additional Information:
Yinghui Kong, Li Li, Ke Zhang, Qiang Ni, and Jungong Han "Attention module-based spatial–temporal graph convolutional networks for skeleton-based action recognition," Journal of Electronic Imaging 28(4), 043032 (30 August 2019). https://doi.org/10.1117/1.JEI.28.4.043032 Copyright notice format: Copyright 2019 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. DOI abstract link format: http://dx.doi.org/DOI# (Note: The DOI can be found on the title page or online abstract page of any SPIE article.)
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
137355
Deposited By:
Deposited On:
09 Oct 2019 08:45
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Sep 2020 05:14