Special Section on Video Surveillance and Transportation Imaging Applications

Visual traffic surveillance framework: classification to event detection

[+] Author Affiliations
Amol Ambardekar

University of Nevada, Reno, Department of Computer Science and Engineering, Reno, Nevada 89557

Mircea Nicolescu

University of Nevada, Reno, Department of Computer Science and Engineering, Reno, Nevada 89557

George Bebis

University of Nevada, Reno, Department of Computer Science and Engineering, Reno, Nevada 89557

Monica Nicolescu

University of Nevada, Reno, Department of Computer Science and Engineering, Reno, Nevada 89557

J. Electron. Imaging. 22(4), 041112 (Aug 28, 2013). doi:10.1117/1.JEI.22.4.041112
History: Received April 16, 2013; Revised July 12, 2013; Accepted July 26, 2013
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Abstract.  Visual traffic surveillance using computer vision techniques can be noninvasive, automated, and cost effective. Traffic surveillance systems with the ability to detect, count, and classify vehicles can be employed in gathering traffic statistics and achieving better traffic control in intelligent transportation systems. However, vehicle classification poses a difficult problem as vehicles have high intraclass variation and relatively low interclass variation. Five different object recognition techniques are investigated: principal component analysis (PCA)+difference from vehicle space, PCA+difference in vehicle space, PCA+support vector machine, linear discriminant analysis, and constellation-based modeling applied to the problem of vehicle classification. Three of the techniques that performed well were incorporated into a unified traffic surveillance system for online classification of vehicles, which uses tracking results to improve the classification accuracy. To evaluate the accuracy of the system, 31 min of traffic video containing multilane traffic intersection was processed. It was possible to achieve classification accuracy as high as 90.49% while classifying correctly tracked vehicles into four classes: cars, SUVs/vans, pickup trucks, and buses/semis. While processing a video, our system also recorded important traffic parameters such as the appearance, speed, trajectory of a vehicle, etc. This information was later used in a search assistant tool to find interesting traffic events.

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Citation

Amol Ambardekar ; Mircea Nicolescu ; George Bebis and Monica Nicolescu
"Visual traffic surveillance framework: classification to event detection", J. Electron. Imaging. 22(4), 041112 (Aug 28, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.041112


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