A Vehicle Re-identification Post-processing Algorithm Against Pedestrian Interference

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A Vehicle Re-identification Post-processing Algorithm Against Pedestrian Interference

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[1]CAO Yangjie,CAI Jihao,WANG Peiqi,et al.A Vehicle Re-identification Post-processing Algorithm Against Pedestrian Interference[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-7.[doi:10.13705/j.issn.1671-6833.2025.05.012]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 XX Page number: 1-7 Column: Public date: 2026-09-10

Title:: A Vehicle Re-identification Post-processing Algorithm Against Pedestrian Interference

Author(s):: CAO Yangjie¹; 2; CAI Jihao¹; 2; WANG Peiqi¹; 2; Yang Cong¹; 2; 1.School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China;2.Henan Province Engineering Research Center of Intelligent Sensing and Processing, Zhengzhou 450002

Keywords:: vehicle re-identification; pedestrian interference; track refinement; background suppression; feature extraction

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2025.05.012

Abstract:: Vehicle re-identification serves as the foundation for vehicle tracking. However, pedestrian interference was found to significantly degrade the quality of feature extraction, thereby reducing the accuracy and precision of vehicle re-identification. To address this issue, a vehicle re-identification algorithm named TRaBS was proposed, which incorporates trajectory optimization and background suppression techniques. First, ResNeXt101-IBN-a was employed to extract initial features from vehicle images, while a background suppression algorithm was applied to the camera frames to generate background-suppressed vehicle features. Second, to mitigate the impact of pedestrian interference on feature extraction, Euclidean distance and Gaussian kernel functions were utilized to replace image-level features with more stable trajectory-level representations. Through these techniques, the problem of pedestrian-induced interference in vehicle re-identification was effectively alleviated. To comprehensively evaluate the effectiveness of TRaBS in handling pedestrian interference and its generalization ability in interference-free scenarios, extensive comparative and ablation experiments were conducted. Experimental results demonstrated that the vehicle re-identification model integrated with trajectory optimization and background suppression achieved significant performance improvements on both benchmark datasets and derivative datasets with pedestrian interference. Specifically, on the VeRi-776 dataset, the model achieved a mean Average Precision (mAP) of 83.6% and a Rank-1 accuracy of 97.6%, outperforming existing state-of-the-art methods.

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