A New Approach for Vehicle Recognition and Tracking in Multi-camera Traffic System.

In order to ensure recognition accuracy, intelligent traffic video tracking system usually requires various types of information. Therefore, multi-features fusion becomes a good choice. In this paper, a new recognition approach for vehicle types based multi-feature fusion is proposed, which is used for vehicle tracking in a multi-camera traffic system. An improved Canny operator is presented for edge detection. SURF (Speeded Up Robust Features) is used for local feature extraction. To improve the performance of distance calculation between features, a refined method based on Hellinger kernel is put forward. A position constraint rule is applied to reduce unnecessary fake matchings. Finally, the information of vehicle types combined with LBP (Local Binary Pattern), HOG (Histogram of Oriented Gradients) is used for a multi-camera vehicle tracking platform, which adopts Hadoop to realize the parallel computing of the system. Experimental results show that the proposed approach has good performance for the platform.