scholarly journals CLUST: Simulating Realistic Crowd Behaviour by Mining Pattern from Crowd Videos

2017 ◽  
Vol 37 (1) ◽  
pp. 184-201 ◽  
Author(s):  
M. Zhao ◽  
W. Cai ◽  
S. J. Turner
Keyword(s):  
Crowd Behaviour ◽  
Mining Pattern

Simulating Crowd Behaviour Combining Both Microscopic and Macroscopic Rules

2021 ◽  
Author(s):  
R.A. Saeed ◽  
Diego Reforgiato Recupero ◽  
Paolo Remagnino
Keyword(s):  
Crowd Behaviour

scholarly journals Crowd behaviour during high-stress evacuations in an immersive virtual environment

2016 ◽  
Vol 13 (122) ◽  
pp. 20160414 ◽  
Author(s):  
Mehdi Moussaïd ◽  
Mubbasir Kapadia ◽  
Tyler Thrash ◽  
Robert W. Sumner ◽  
Markus Gross ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Virtual Environment ◽  
Human Subjects ◽  
High Stress ◽  
Three Dimensional ◽  
Real Life ◽  
Social Conventions ◽  
Emergency Situations ◽  
Crowd Behaviour ◽  
The Individual

Understanding the collective dynamics of crowd movements during stressful emergency situations is central to reducing the risk of deadly crowd disasters. Yet, their systematic experimental study remains a challenging open problem due to ethical and methodological constraints. In this paper, we demonstrate the viability of shared three-dimensional virtual environments as an experimental platform for conducting crowd experiments with real people. In particular, we show that crowds of real human subjects moving and interacting in an immersive three-dimensional virtual environment exhibit typical patterns of real crowds as observed in real-life crowded situations. These include the manifestation of social conventions and the emergence of self-organized patterns during egress scenarios. High-stress evacuation experiments conducted in this virtual environment reveal movements characterized by mass herding and dangerous overcrowding as they occur in crowd disasters. We describe the behavioural mechanisms at play under such extreme conditions and identify critical zones where overcrowding may occur. Furthermore, we show that herding spontaneously emerges from a density effect without the need to assume an increase of the individual tendency to imitate peers. Our experiments reveal the promise of immersive virtual environments as an ethical, cost-efficient, yet accurate platform for exploring crowd behaviour in high-risk situations with real human subjects.

scholarly journals A Framework for Data Mining Pattern Management

2004 ◽  
pp. 87-98 ◽  
Author(s):  
Barbara Catania ◽  
Anna Maddalena ◽  
Maurizio Mazza ◽  
Elisa Bertino ◽  
Stefano Rizzi
Keyword(s):  
Data Mining ◽  
Mining Pattern

Identification of crowd behaviour patterns using stability analysis

2021 ◽  
pp. 1-15
Author(s):  
V. Muhammed Anees ◽  
G. Santhosh Kumar
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Security Requirements ◽  
Video Sequences ◽  
World Population ◽  
System Modelling ◽  
Intelligent Surveillance ◽  
Flow Modelling ◽  
Wide Range ◽  
Crowd Behaviour

Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.

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