Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement

Biao Zheng, Luefeng Chen*, Min Wu, Witold Pedrycz, Kaoru Hirota

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

With the development of science and technology, graph convolutional network has made great progress in improving the accuracy of action recognition. However, there still exists some deficiencies in current methods. Firstly, the human skeleton point coordinates entering into the network are barely refined, which may cause large error. Secondly, the second-order infor-mation(the length and direction of bones), which can reflect action characteristics discriminatively, is rarely used. To solve the above issues, a two stream graph convolutional network with pose refinement for skeleton based action recognition is proposed. Besides, we use an adaptive block to to help improve the accuracy. We test our method on Kinetics dataset and the experiment show it can get better results than some recent methods, which plays a positive role in future research.

源语言英语
主期刊名Proceedings of the 41st Chinese Control Conference, CCC 2022
编辑Zhijun Li, Jian Sun
出版商IEEE Computer Society
6353-6356
页数4
ISBN(电子版)9789887581536
DOI
出版状态已出版 - 2022
已对外发布
活动41st Chinese Control Conference, CCC 2022 - Hefei, 中国
期限: 25 7月 202227 7月 2022

出版系列

姓名Chinese Control Conference, CCC
2022-July
ISSN(印刷版)1934-1768
ISSN(电子版)2161-2927

会议

会议41st Chinese Control Conference, CCC 2022
国家/地区中国
Hefei
时期25/07/2227/07/22

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引用此

Zheng, B., Chen, L., Wu, M., Pedrycz, W., & Hirota, K. (2022). Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement. 在 Z. Li, & J. Sun (编辑), Proceedings of the 41st Chinese Control Conference, CCC 2022 (页码 6353-6356). (Chinese Control Conference, CCC; 卷 2022-July). IEEE Computer Society. https://doi.org/10.23919/CCC55666.2022.9901587