Special Section on Intelligent Surveillance for Transport Systems

Vision-based traffic surveys in urban environments

[+] Author Affiliations
Zezhi Chen, Tim Ellis

Kingston University, School of Computer Science and Mathematics, Digital Information Research Centre, Penrhyn Road, Kingston-upon-Thames, Surrey KT1 2EEUnited Kingdom

Sergio A. Velastin

Universidad Carlos III de Madrid, Applied Artificial Intelligence Research Group, Av. de la Universidad Carlos III, Colmenarejo, Madrid, Spain

J. Electron. Imaging. 25(5), 051206 (Apr 28, 2016). doi:10.1117/1.JEI.25.5.051206
History: Received December 20, 2015; Accepted March 23, 2016
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Abstract.  This paper presents a state-of-the-art, vision-based vehicle detection and type classification to perform traffic surveys from a roadside closed-circuit television camera. Vehicles are detected using background subtraction based on a Gaussian mixture model that can cope with vehicles that become stationary over a significant period of time. Vehicle silhouettes are described using a combination of shape and appearance features using an intensity-based pyramid histogram of orientation gradients (HOG). Classification is performed using a support vector machine, which is trained on a small set of hand-labeled silhouette exemplars. These exemplars are identified using a model-based preclassifier that utilizes calibrated images mapped by Google Earth to provide accurately surveyed scene geometry matched to visible image landmarks. Kalman filters track the vehicles to enable classification by majority voting over several consecutive frames. The system counts vehicles and separates them into four categories: car, van, bus, and motorcycle (including bicycles). Experiments with real-world data have been undertaken to evaluate system performance and vehicle detection rates of 96.45% and classification accuracy of 95.70% have been achieved on this data.

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Citation

Zezhi Chen ; Tim Ellis and Sergio A. Velastin
"Vision-based traffic surveys in urban environments", J. Electron. Imaging. 25(5), 051206 (Apr 28, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.051206


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