Specific vehicle detection and tracking in road environment

Authors:
Yang Zhang;Jinqiao Wang;Wei Fu;Hanqing Lu;Huazhong Xu
Affiliations:
Chinese Academy of Sciences, and Wuhan University of Technology;Chinese Academy of Sciences;Chinese Academy of Sciences;Chinese Academy of Sciences;Wuhan University of Technology
Venue:
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Year:
2011

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Abstract

In this paper, we propose a real-time method to detect and track specific vehicles, toward monitoring the abnormal activities in the traffic environment. Firstly, a novel background subtraction approach is used to get the accurate foreground segmentations and shadow suppression. Then a HIK (Histogram Intersection Kernel) based SVM classifier is trained to recognize whether a vehicle is suspicious. Finally, the Camshift based tracking is used to fast track the specific vehicles. Experiments in a real traffic scenario show the promise of the proposed approach.