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At present, vehicle tracking has been realized in many fields. The applications of vehicle tracking include Advanced Driver Assisted System and live broadcast of automobile events. After reading the paper's Intelligent vehicle pedestrian tracking based on YOLOv3 and DASiamRPN, we learn that they propose a pedestrian tracking algorithm which combines YOLOv3 and DASiamRPN to realize the designated pedestrian tracking. Finally, the pedestrian tracking is successfully realized, which ensures the tracking accuracy. However, while exploring the guarantee of accuracy, there are few studies on how to achieve successful tracking at different tracking speeds. The most difficult part about vehicle tracking is fast vehicle tracking. High speed often makes the system lose tracking target. So, in this paper, we studied how to implement fast vehicle tracking. However, there are many algorithms for vehicle tracking, such as the Lucas-kanade algorithm, Faster RCNN algorithm, Yolo algorithm, SSD algorithm, and DeepSORT algorithm. More and more new algorithms have appeared in recent years. But through experiments, we found that there are some vehicles tracking algorithms such as the Lucas-kanade algorithm, which cannot achieve accurate vehicle tracking in the case of rapid vehicle movement. Therefore, we listed and studied a variety of algorithms for vehicle tracking. After querying the papers., comparing and analyzing, one of the appropriate algorithms was selected: DeepSORT and Yolo-V5 algorithm. By analyzing the principles and image processing flow of DeepSORT and Yolo-V5 algorithm, we know that this algorithm is updated and more reliable. After experiments, it successfully tracks fast moving vehicles in a given video. After statistics, the successful tracking time, testing time and accuracy of the video meet the successful tracking standard.
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