In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

Skeleton behavior recognition combining adaptive local graph convolution with multi-scale time modeling

Tian Qing
Yu Jingjing
Zhang Zheng
School of Information, North China University of Technology, Beijing 100144, China
DOI: 10.19734/j.issn.1001-3695.2024.08.0370

Abstract

Given the inherent topological structure characteristics of the human skeleton, researchers effectively model skeleton data using graph convolution networks for behavior recognition. However, challenges arise in skeleton behavior recognition methods because time convolution relies on a fixed topological graph structure and fixed kernel size, which makes it difficult to adapt to variable action types, postures, and behavioral durations. This reliance leads to modeling errors and affects recognition accuracy. To tackle this issue, we propose a skeleton behavior recognition method that combines adaptive local graph convolution with multi-scale temporal modeling. Our method allows for the independent dynamic characterization of the human skeletal structure through the adaptive local graph convolution module. We designed the multi-scale temporal modeling module to accommodate behaviors of varying durations while reducing the number of parameters and computational complexity. Furthermore, we introduce the spatio-temporal DropGraph structure to dynamically adjust the graph topology, which improves the model's generalization ability and prevents overfitting. Our experiments show that we achieved accuracy rates of 93.39% and 97.18% under the cross-object C-Sub and cross-view C-View benchmarks for the NTU RGB+D 60 dataset, respectively, and 90.48% and 91.95% under the cross-object C-Sub and cross-set C-Set benchmarks for the NTU RGB+D 120 dataset, respectively. These results outperform those of existing behavioral recognition methods, proving the superiority of our approach.

Foundation Support

国家重点研发计划资助项目(2020YFB1600702)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.08.0370
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-06] Accepted Paper

Cite This Article

田青, 虞静静, 张正. 结合自适应局部图卷积与多尺度时间建模的骨架行为识别 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0370. (Tian Qing, Yu Jingjing, Zhang Zheng. Skeleton behavior recognition combining adaptive local graph convolution with multi-scale time modeling [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0370. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)