Skeleton Based Action Recognition Algorithm on Multi-modal Lightweight Graph Convolutional Network

Compared with the traditional RGB-based methods, the skeleton-based action recognition methods have become the main research direction in the field of computer vision in recent years because they are less affected by many factors such as illumination, viewing angle and background complexity. However...

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Bibliographic Details
Published inJisuanji kexue yu tansuo Vol. 15; no. 4; pp. 733 - 742
Main Author SU Jiangyi, SONG Xiaoning, WU Xiaojun, YU Dongjun
Format Journal Article
LanguageChinese
Published Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.04.2021
Subjects
action recognition
graph convolutional network
human skeleton
lightweight
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Summary:Compared with the traditional RGB-based methods, the skeleton-based action recognition methods have become the main research direction in the field of computer vision in recent years because they are less affected by many factors such as illumination, viewing angle and background complexity. However, the current skeleton-based methods still have some problems such as large parameters, long time-consuming and high computational complexity, which makes it complicated and difficult to meet the requirements of efficiency and accuracy simultaneously. To address these issues, a lightweight graph convolution network using multi-modal data fusion is proposed. Firstly, the multi-modal information flow data are fused by multi-modal data fusion method. Secondly, the spatial and temporal information of human joints are extracted using spatial and temporal modules respectively. Finally, the classification results are obtained through the fully connected layer. Experimental results conducted on the two commonly used datase
ISSN:1673-9418
DOI:10.3778/j.issn.1673-9418.2008051