Technology of Graphic & Image
|
2479-2484

GSTIN with spatiotemporal feature fusion for video action recognition

Li Kewen1
Zhang Zhentao1
Li Sujie2
Lei Yongxiu1
1. College of Computer Science & Technology, China University of Petroleum(East China), Qingdao Shandong 266580, China
2. SINOPEC Pipeline Storage & Transportation Co. , Ltd. , Xuzhou Jiangsu 221008, China

Abstract

Video action recognition is a very challenging topic in the field of computer vision. The main task is to use the deep information by intelligent video analysis technology such as deep learning to recognize the human behavior. To further improve the performance of the two main frameworks, this paper proposed GSTIN for spatiotemporal feature fusion. GSTIN designed a spatiotemporal feature fusion module InBST, which could make network obtain the interactive temporal and spatial information. Based on the spatiotemporal feature fusion module InBST, GSTIN constructed a multi branch GSTIN suitable for action recognition. GSTIN was tested two classic video action recognition datasets UCF101 and HMDB51. Compared with the action recognition networks, experimental results show that GSTIN has better recognition performance.

Foundation Support

国家自然科学基金重大项目(51991361)
国家自然科学基金资助项目(61673396)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.08.0392
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 8
Section: Technology of Graphic & Image
Pages: 2479-2484
Serial Number: 1001-3695(2021)08-043-2479-06

Publish History

[2021-08-05] Printed Article

Cite This Article

李克文, 张震涛, 李素杰, 等. 面向时空特征融合的GSTIN动作识别网络 [J]. 计算机应用研究, 2021, 38 (8): 2479-2484. (Li Kewen, Zhang Zhentao, Li Sujie, et al. GSTIN with spatiotemporal feature fusion for video action recognition [J]. Application Research of Computers, 2021, 38 (8): 2479-2484. )

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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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