Crowd behavior analysis: A review where physics meets biology

Kok Ven Jyn , Mei Kuan Lim, Chee Seng Chan

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irrevocable crowd disasters. The two-fold increase of carnage in crowd since the past two decades has spurred significant advances in the field of computer vision, towards effective and proactive crowd surveillance. Computer vision studies related to crowd are observed to resonate with the understanding of the emergent behavior in physics (complex systems) and biology (animal swarm). These studies, which are inspired by biology and physics, share surprisingly common insights, and interesting contradictions. However, this aspect of discussion has not been fully explored. Therefore, this survey provides the readers with a review of the state-of-the-art methods in crowd behavior analysis from the physics and biologically inspired perspectives. We provide insights and comprehensive discussions for a broader understanding of the underlying prospect of blending physics and biology studies in computer vision.

Original languageEnglish
Pages (from-to)342-362
Number of pages21
JournalNeurocomputing
Volume177
DOIs
Publication statusPublished - 12 Feb 2016
Externally publishedYes

Fingerprint

Physics
Computer vision
Systems Biology
Disasters
Large scale systems
Animals

Keywords

  • Biologically inspired
  • Computer vision
  • Crowd behavior analysis
  • Physics-inspired
  • Survey

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Crowd behavior analysis : A review where physics meets biology. / Ven Jyn , Kok; Lim, Mei Kuan; Chan, Chee Seng.

In: Neurocomputing, Vol. 177, 12.02.2016, p. 342-362.

Research output: Contribution to journalArticle

Ven Jyn , Kok ; Lim, Mei Kuan ; Chan, Chee Seng. / Crowd behavior analysis : A review where physics meets biology. In: Neurocomputing. 2016 ; Vol. 177. pp. 342-362.
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