Occluded vehicle tracking by integrating full-scale features and trajectory correction

In order to improve the tracking drift and identity switching (IDs) problems caused by occlusion in vehicle tracking, a method for tracking occluded vehicles that integrates full-scale features and trajectory correction is proposed based on the Deep SORT algorithm. Firstly, a full-scale feature extraction network is introduced to extract the features of different scales of the target and realize adaptive dynamic fusion to enhance the target appearance characteristics. Then, a trajectory correction algorithm is proposed to repair the tracking trajectory during occlusion, and the Kalman filter parameters are updated to reduce the accumulated linear error during occlusion and optimize the target motion characteristics. Finally, the occluded vehicle is tracked by combining appearance features and motion features. The feasibility of the proposed method is verified by ablation experiments and visualization analysis. The experimental results on the KITTI dataset show that compared with the existing typical methods, the proposed method achieves the highest comprehensive score of 78.13 and the lowest number of IDs of 192, which effectively improves the IDs problem in occluded vehicle tracking and improves the robustness of vehicle tracking.