Survey of Research on Crowd Congestion Detection in Dense Scenarios

Perceiving and detecting crowd congestion in public spaces is an extremely challenging task in computer vision. Research on this issue, such as analyzing the motion characteristics of crowds and constructing behavior detection models, can provide valuable insights into the motion traits and behavioral essence of crowd activities in dense scenarios. Additionally, it can assist relevant public safety departments in formulating management strategies and emergency response measures, thereby effectively preventing the occurrence and escalation of crowd-related disasters. To this end, this paper summarizes the research efforts on dense crowd congestion detection. First, an overview of the qualitative characteristics of crowd congestion from the perspectives of crowd dynamics, social force models, and fluid mechanics theory is presented. Second, existing crowd congestion detection algorithms and related computational models are investigated. Next, the public datasets and model performance evaluation methods relevant to this research are presented. Finally, the application scenarios and future research directions for crowd congestion detection are explored. A review of the current research status on the qualitative and quantitative analyses of dense crowd congestion behaviors in public spaces offers valuable references for crowd activity perception, behavior analysis and understanding, and anomaly detection in fields such as computer vision, intelligent surveillance, and artificial intelligence.