Special Section on Intelligent Surveillance for Transport Systems

Automatic vehicle counting system for traffic monitoring

[+] Author Affiliations
Alain Crouzil

Université Paul Sabatier, Institut de Recherche en Informatique de Toulouse, 118 route de Narbonne, 31062 Toulouse Cedex 9, France

Louahdi Khoudour

Center for Technical Studies of South West, ZELT Group, 1 avenue du Colonel Roche, 31400 Toulouse, France

Paul Valiere

Sopra Steria, 1 Avenue André-Marie Ampère, 31770 Colomiers, France

Dung Nghy Truong Cong

Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet Street, 10th District, Ho Chi Minh City, Vietnam

J. Electron. Imaging. 25(5), 051207 (Jun 01, 2016). doi:10.1117/1.JEI.25.5.051207
History: Received January 7, 2016; Accepted April 27, 2016
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Abstract.  The article is dedicated to the presentation of a vision-based system for road vehicle counting and classification. The system is able to achieve counting with a very good accuracy even in difficult scenarios linked to occlusions and/or presence of shadows. The principle of the system is to use already installed cameras in road networks without any additional calibration procedure. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. First, the approach models each pixel of the background with an adaptive Gaussian distribution. This model is coupled with a motion detection procedure, which allows correctly location of moving vehicles in space and time. The nature of trials carried out, including peak periods and various vehicle types, leads to an increase of occlusions between cars and between cars and trucks. A specific method for severe occlusion detection, based on the notion of solidity, has been carried out and tested. Furthermore, the method developed in this work is capable of managing shadows with high resolution. The related algorithm has been tested and compared to a classical method. Experimental results based on four large datasets show that our method can count and classify vehicles in real time with a high level of performance (>98%) under different environmental situations, thus performing better than the conventional inductive loop detectors.

© 2016 SPIE and IS&T

Citation

Alain Crouzil ; Louahdi Khoudour ; Paul Valiere and Dung Nghy Truong Cong
"Automatic vehicle counting system for traffic monitoring", J. Electron. Imaging. 25(5), 051207 (Jun 01, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.051207


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