Simulation of Crowd Problems for Computer Vision

This work presents an approach for generating video evidence of dangerous situations in crowded scenes. The scenarios of interest are those with high safety risk such as blocked exit, collapse of a person in the crowd, and escape panic. Real visual evidence for these scenarios is rare or unsafe to reproduce in a controllable way. Thus there is a need for simulation to allow training and validation of computer vision systems applied to crowd monitoring. The results shown here demonstrate how to simulate the most important aspects of crowds for performance analysis of computer based video surveillance systems.

[1]  Wen Tang,et al.  Real-time crowd movement on large scale terrains , 2003, Proceedings of Theory and Practice of Computer Graphics, 2003..

[2]  R. Hughes.  The flow of human crowds , 2003 .

[3]  Céline Loscos,et al.  Intuitive crowd behavior in dense urban environments using local laws , 2003, Proceedings of Theory and Practice of Computer Graphics, 2003..

[4]  James M. Rehg,et al.  A data-driven approach to quantifying natural human motion , 2005, ACM Trans. Graph..

[5]  Lisa Gralewski,et al.  Theory and Practice of Computer Graphics , 2004 .

[6]  Michel Bierlaire,et al.  Simulation of Pedestrian Behaviour using a Discrete Choice Model Calibrated on Actual Motion Data , 2004 .

[7]  Sergio A. Velastin,et al.  Motion-based machine vision techniques for the management of large crowds , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).

[8]  Sergio A. Velastin,et al.  PRISMATICA: toward ambient intelligence in public transport environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[10]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[11]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[12]  Graham Coleman,et al.  Detection and explanation of anomalous activities: representing activities as bags of event n-grams , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Aaron F. Bobick,et al.  Recognition of Visual Activities and Interactions by Stochastic Parsing , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Matthew Brand,et al.  Discovery and Segmentation of Activities in Video , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Shaogang Gong,et al.  Discovering Bayesian causality among visual events in a complex outdoor scene , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[17]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interaction , 1999, ICVS.

[19]  David C. Hogg,et al.  Learning Variable-Length Markov Models of Behavior , 2001, Comput. Vis. Image Underst..

[20]  Takashi Chikayama,et al.  Simulating the Collision Avoidance Behavior of Pedestrians , 2000 .

[21]  James M. Rehg,et al.  A data-driven approach to quantifying natural human motion , 2005, SIGGRAPH '05.