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
Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 3
911-919

Neural network for detecting crowd convergence behavior based on locust visual nerve

Yang Xutaoa,b
Qin Jina,b
Hu Bina,b,c
a. State Key Laboratory of Public Big Data, b. College of Computer Science & Technology, c. Artificial Intelligence Research Institute, Guizhou University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0224

Abstract

Moving crowd in public place can generate a unique movement pattern known as crowd convergence behavior at intersections or passageways, which can lead to potential public risks, such as crowding, pushing, and trampling. However, no computational models have been reported against to detecting crowd convergence. To fill this gap, this paper investigated a bio-inspired artificial visual neural network which named crowd convergence behavior detection neural network(CCBDNN). Based on the visual neural structure characteristics of locusts, CCBDNN perceives visual motion cues produced by crowd activities within the field of view and extracted local motion direction cues of crowd using the direction perception mechanism of mammalian retinas, and then constructed a spike mechanism and output neural spiking responding to represent the perception of crowd convergence behavior by the danger perception mechanism of LGMD neurons in locusts' vision systems. Systematic experiments show that CCBDNN can effectively detect crowd convergence behavior in visual scenes and produce output tuning with unique preference characteristics. This paper is involved the dynamic visual information processing inspired by biological neural mechanisms, which can provide new methods and ideas for crowd activity detection and behavior recognition in artificial intelligence.

Foundation Support

国家自然科学基金资助项目(62066006)
贵州省自然科学基金资助项目(黔科合基础［2020］1Y261)
贵州省科技计划资助项目(黔科合支撑［2020］3Y004号)
贵州大学引进人才科研项目(贵大人基合字(2019)58号)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0224
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 3
Section: Technology of Graphic & Image
Pages: 911-919
Serial Number: 1001-3695(2025)03-035-0911-09

Publish History

[2025-03-05] Printed Article

Cite This Article

杨旭涛, 秦进, 胡滨. 基于蝗虫视觉神经的人群汇流行为检测神经网络 [J]. 计算机应用研究, 2025, 42 (3): 911-919. (Yang Xutao, Qin Jin, Hu Bin. Neural network for detecting crowd convergence behavior based on locust visual nerve [J]. Application Research of Computers, 2025, 42 (3): 911-919. )

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)