View-Aware-Based Post-Processing for Vehicle Re-Identification

Zhijun Hu

doi:10.1109/TITS.2024.3484374

2025-01-01

PRE

DOI：10.1109/TITS.2024.3484374

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The traditional view-aware based vehicle re-identification methods integrated the vehicle view information into the training process, allowing the model parameters to learn the vehicle view knowledge. So, the extracted vehicle features contained vehicle view information, and the impact of view differences on model performance can be reduced. Since ensemble training with different methods will affect each other, it is difficult for these methods to train models together with other non view-aware methods. In order to address this challenge, in this paper, we propose a view-aware-based post-processing method (VABPP), which uses vehicle view information to re-rank the re-identification results during testing. According to vehicle views, VABPP method divide the distances between vehicle id features into several groups. During testing, multiply the distances of each different group by different coefficients. So, it treats different views equally. In order to achieve better implementation of this post-processing method, in this paper, we also propose a scheme that unifies the feature distance distributions of the training set and the test set. This scheme can enable some properties of the training set to be directly used in the test set. As the properties of the training set are trained under the guidance of correctly labeled labels, which enhances the robustness of the test set properties. The mAP in VeRi-776 dataset and in the three test sets of VERI-Wild dataset are 83.7%, 88.7%, 84.6% and 78.5%, respectively. And the rank-1 in the three test sets of VehicleID dataset are 88.6%, 85.4% and 81.1%, respectively.

Keyword:

Training

Probes

Testing

Mathematical models

Feature extraction

Automobiles

Measurement

Image coding

Deep learning

Computer architecture

Vehicle re-identification