Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 10
3173-3179

Scene relation graph network for group activity recognition

Jiao Chang
Wu Kewei
Yu Lei
Xie Zhao
Li Wenzhong
School of Computer Science & Information Engineering, Hefei University of Technology, Hefei 230601, China
DOI: 10.19734/j.issn.1001-3695.2022.12.0828

Abstract

To solve the problem of inaccurate description and unreliable relation inference in group activity recognition, this paper focused on constructing a scene relationship graph for three aspects: individual, group, and scene, and proposed a scene relationship graph network(SRGN) for group activity recognition. This method included a feature extraction module, a scene relation graph inference module, and a classification module. The feature extraction module extracted individual features, group features, and scene features by convolutional neural network. To fully explore the impact of scene on individual and group descriptions, the scene relation graph inference module learnt individual features and group features by building individual-scene and group-scene relationship graphs in a two-branch framework. Scene graph inference took into account the influence of individual on group and introduced a cross-branch module. It used the classification module to classify individual features and group features for prediction. The experimental results show that the group recognition accuracy of the proposed method on volleyball and collective activity data sets is improved by 1.1% and 0.5%, respectively. It verifies the validity of the scene graph in describing individual feature and group feature.

Foundation Support

安徽省重点研究与开发计划资助项目(202004d07020004)
安徽省自然科学基金资助项目(2108085MF203)
中央高校基本科研业务费专项资金资助项目(PA2021GDSK0072,JZ2021HGQA0219)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.12.0828
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 10
Section: Technology of Graphic & Image
Pages: 3173-3179
Serial Number: 1001-3695(2023)10-045-3173-07

Publish History

[2023-03-16] Accepted Paper
[2023-10-05] Printed Article

Cite This Article

焦畅, 吴克伟, 于磊, 等. 场景关系图学习的群组行为识别 [J]. 计算机应用研究, 2023, 40 (10): 3173-3179. (Jiao Chang, Wu Kewei, Yu Lei, et al. Scene relation graph network for group activity recognition [J]. Application Research of Computers, 2023, 40 (10): 3173-3179. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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