Specific vehicle detection and tracking in road environment
Yang Zhang, Jinqiao Wang, Wei Fu, Hanqing Lu, Huazhong Xu · 2011
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.