Feature-based efficient vehicle tracking for a traffic surveillance system

Abstract

This paper presents an efficient feature-based tracking system to detect vehicles in a number of challenging conditions like lighting, occlusion, and darkness. A novel approach for vehicle tracking is proposed using an unsupervised feature matching technique. The system is fully functional under varying conditions because most of the salient vehicle features are tracked from matching of features in different objects. A feature-based vehicle tracking is proposed for a real-time traffic surveillance system. By analyzing the features of vehicles and their corresponding matched features, salient discriminative features of vehicles are calculated. The tracking of target vehicles is performed from the calculation of winner pixels in the consecutive frames using an unsupervised feature matching. To increase the accuracy of vehicle feature classification, orientation of feature descriptor of target vehicles tracked in the video frames is taken into consideration. Experimental results show that features classification rates of 96.4% and 92.7% for different vehicle sets can be achieved using the feature of aspect ratio. The proposed method is compared with recent feature-based method and Kalman filter-based method that results into better detection performance. The method can track the target vehicle under different situations like rotation, scaling, illumination and many others requiring less computation and providing better accuracy.