Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras
This addresses the challenge of identifying vehicles across cameras in intelligent transportation systems, but it is incremental as it builds on existing re-ranking and feature extraction techniques.
The paper tackled vehicle re-identification by proposing a method that uses vehicle attributes (orientation and brand) and camera mutual exclusion theory to re-rank results, achieving mAP of 63.73% and rank-1 accuracy of 76.61% on the CVPR 2021 AI City Challenge.
Vehicle Re-identification aims to identify a specific vehicle across time and camera view. With the rapid growth of intelligent transportation systems and smart cities, vehicle Re-identification technology gets more and more attention. However, due to the difference of shooting angle and the high similarity of vehicles belonging to the same brand, vehicle re-identification becomes a great challenge for existing method. In this paper, we propose a vehicle attribute-guided method to re-rank vehicle Re-ID result. The attributes used include vehicle orientation and vehicle brand . We also focus on the camera information and introduce camera mutual exclusion theory to further fine-tune the search results. In terms of feature extraction, we combine the data augmentations of multi-resolutions with the large model ensemble to get a more robust vehicle features. Our method achieves mAP of 63.73% and rank-1 accuracy 76.61% in the CVPR 2021 AI City Challenge.