| Citation: | GUO J F，ZHANG Z H. Track obscured vehicles by fusing full-scale features with trajectory correction[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（5）：1608-1619 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0288
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An occluded vehicle tracking method that fuses full-scale features with trajectory correction based on the Deep SORT algorithm is proposed to improve the tracking drift and identity switching (IDS) problems caused by occlusion in vehicle tracking. First, a full-scale feature extraction network is introduced to extract features of different scales of the target and to achieve adaptive dynamic fusion to enhance the apparent features of the target. Then, a trajectory correction approach is suggested to fix the tracking trajectory during the occlusion process, and the Kalman filter parameters are adjusted in order to minimize the cumulative linear errors during the occlusion process and optimize the target motion features. Finally, occluded vehicle tracking is achieved by combining the appearance features and the motion features. The feasibility of the proposed method is verified by ablation experiments and visualized analysis. The suggested approach successfully resolves the IDS issue in obscured vehicle tracking and increases the robustness of vehicle tracking, as demonstrated by experimental results on the KITTI dataset, which yield the highest overall score of 78.13% and the fewest number of IDS (192), when compared to typical existing methods.
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