Abstract: In complex road scenes, frequent occlusions and similar appearances between vehicle targets, coupled with the use of static preset parameters used throughout the entire movement of the targets collectively contribute to a decline in tracking accuracy. This paper proposes an object tracking algorithm based on vehicle appearance features and inter-frame optical flow. Firstly, the position information of the vehicle target frame is obtained through the YOLOv5x network model. Secondly, the optical flow between the current frame and the previous frame is calculated using the RAFT (recurrent all-pairs field transforms for optical flow) algorithm, and the optical flow map is clipped according to the obtained position information. Finally, in the process of Kalman filtering, inter-frame optical flow is used to compensate for more accurate motion state information, while vehicle appearance features and intersection over union (IOU) features are used to complete trajectory matching. Experimental results show that the tracking algorithm correlating inter-frame optical flow performs well on the MOT16 data set. Compared with simple online and realtime tracking with a deep association metric (DeepSORT), mostly tracked trajectories (MT) has increased by 1.6%, multiple object tracking accuracy (MOTA) has increased by 1.3%, and multiple object tracking precision (MOTP) has increased by 0.6%. The accuracy of the improved vehicle appearance feature extraction model has been improved by 1.7% and 6.3% on the training and verification sets, respectively. Consequently, leveraging the high-precision vehicle appearance feature model and motion state information from the associated inter-frame optical flow enables effective vehicle target tracking in traffic scenes.

Key words: object tracking, vehicle exterior features, inter-frame optical flow, Kalman filter