Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
900-905

2D/3D skeleton action recognition based on posture transformation and posture fusion

Zeng Shengqiang
Li Lin
School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2021.07.0286

Abstract

Aiming at the problems that existing human skeleton action recognition methods couldn't explore sufficient human body information and extract sufficient temporal feature, this paper proposed a model based on posture transformation module and posture fusion module(PTF-SGN), which realized the utilization of the key spatio-temporal information in skeleton diagram. Firstly, by preprocessing the skeleton diagram, the model mined the displacement information of limbs and joints, and extracted the features. Then it used the posture transformation module to obtain the posture adjustment factors from the skeleton image data in an unsupervised learning manner, and adaptively adjusted the body posture to enhance the robustness of the model in different environments. Secondly, it proposed a posture fusion module based on the time attention mechanism, which learned the short-term features and the long-term features, and fused the time characteristics of long and short moments to strengthen the characterization ability of time characteristics. Finally, it extracted the global spatio-temporal feature of the skeleton feature to input into the classification network to obtain the action recognition result. The experimental results on the two 3D skeleton datasets of NTU60 RGB+D and NTU120 RGB+D and the two 2D skeleton datasets of Penn-Action and HARPET show that PTF-SGN model can effectively recognize actions of skeleton time series data.

Foundation Support

国家自然科学基金资助项目(61673277)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.07.0286
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Technology of Graphic & Image
Pages: 900-905
Serial Number: 1001-3695(2022)03-045-0900-06

Publish History

[2021-11-07] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

曾胜强, 李琳. 基于姿态校正与姿态融合的2D/3D骨架动作识别方法 [J]. 计算机应用研究, 2022, 39 (3): 900-905. (Zeng Shengqiang, Li Lin. 2D/3D skeleton action recognition based on posture transformation and posture fusion [J]. Application Research of Computers, 2022, 39 (3): 900-905. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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