Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
931-937

Research on collective activity analysis model based on multilevel deep neural network architecture

Pei Lishen1
Zhao Xuezhuan2
Zhang Guohua3
1. School of Computer & Information Engineering, Henan University of Economics & Law, Zhengzhou 450046, China
2. School of Intelligent Engineering, Zhengzhou University of Aeronautic, Zhengzhou 450046, China
3. Institute of Magnetic Levitation & Electromagnetic Propulsion, China Aerospace Institute of Science & Technology, Beijing 100074, China
DOI: 10.19734/j.issn.1001-3695.2021.06.0284

Abstract

Multi-level in-depth analysis of collective activity is an important issue to be solved in the field of activity recognition. Based on the research of deep neural network, this paper proposed a progressive hierarchical analysis model for activity recognition. Using the modulating network based on transfer learning, it detected multi-person with temporal consistency detection in the crowd. Through integrating spatio-temporal feature learning, it recognized the individual actions in the crowd with unconstrained action duration. Through integrating the individual action category, interaction context and scene context, it recognized the crowd activity steady and effectively. A large amount experiments on the benchmark data sets demonstrate that, compared with the current approaches, the proposed model achieves better performance on collective activity analysis and recognition.

Foundation Support

国家自然科学基金资助项目(61806073)
河南省重点研发与推广专项(科技攻关)项目基金资助项目(192102210097,192102210126,212102210160,182102210210)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.06.0284
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Technology of Graphic & Image
Pages: 931-937
Serial Number: 1001-3695(2022)03-050-0931-07

Publish History

[2021-11-06] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

裴利沈, 赵雪专, 张国华. 基于多级深度网络架构的群体行为分析模型研究 [J]. 计算机应用研究, 2022, 39 (3): 931-937. (Pei Lishen, Zhao Xuezhuan, Zhang Guohua. Research on collective activity analysis model based on multilevel deep neural network architecture [J]. Application Research of Computers, 2022, 39 (3): 931-937. )

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)