A Statistical Method for People Counting in Crowded Environments

In this paper we present the results of a two-years research project on automatic people counting in public crowded environments. The aim of the proposed system is to estimate the number of people passing through a gate in a public area such as a metro or a railway station. The problem is particularly challenging due to both the presence of crowd which makes it difficult the use of previous systems based on detection of isolated passengers and to the high level of statistic accuracy requested by traffic monitoring applications (error rate less then 5%).

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[3]  Sung-Jea Ko,et al.  Real-time Vision-based People Counting System for the Security Door , 2002 .

[4]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[5]  Antonio Albiol,et al.  Real-time high density people counter using morphological tools , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[6]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  O. Reiser,et al.  Principles Of Gestalt Psychology , 1936 .

[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]  Tsong-Yi Chen,et al.  An Intelligent People-Flow Counting Method for Passing Through a Gate , 2006, 2006 IEEE Conference on Robotics, Automation and Mechatronics.

[10]  Grégoire Malandain,et al.  3-D chamfer distances and norms in anisotropic grids , 2005, Image Vis. Comput..

[11]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[12]  Michel Desvignes,et al.  People Counting in Transport Vehicles , 2005, WEC.