Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 1
304-307,316

Human action recognition based on deep learning

Li Yupeng
Liu Tingting
Zhang Liang
Tianjin Key Laboratory of Advanced Signal & Image Processing, Civil Aviation University of China, Tianjin 300300, China
DOI: 10.19734/j.issn.1001-3695.2018.05.0499

Abstract

In order to solve the problem of action classification prone to confusion after mapping the four-dimensional depth information to two-dimensional space, this paper proposed a method for human action recognition based on deep learning. Firstly, the method constructed spatially structured dynamic depth images for dimension reduction. Then, it proposed the deep convolution neural network with joint cost function, which combined the cross entropy loss function and the central loss function as cost function, to guide the convolution layer to learn more discriminative deep features. The experimental results evaluated on the public MSRDailyActivity3D and SYSU 3D HOI dataset show that the method obtains a better performance compared with other existing methods, which validates the effectiveness and robustness of the method. The method effectively solves the problem of action classification prone to confusion.

Foundation Support

国家自然科学基金资助项目(61179045)
民航安全能力建设项目(20600523)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0499
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: Technology of Graphic & Image
Pages: 304-307,316
Serial Number: 1001-3695(2020)01-065-0304-04

Publish History

[2020-01-05] Printed Article

Cite This Article

李玉鹏, 刘婷婷, 张良. 基于深度学习的人体动作识别方法 [J]. 计算机应用研究, 2020, 37 (1): 304-307,316. (Li Yupeng, Liu Tingting, Zhang Liang. Human action recognition based on deep learning [J]. Application Research of Computers, 2020, 37 (1): 304-307,316. )

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  • Application Research of Computers Monthly Journal
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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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