Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras

• URL: http://arxiv.org/abs/2104.14882v1
• Date: Fri, 30 Apr 2021 10:11:46 GMT
• Title: Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras
• Authors: Junru Chen, Shiqing Geng, Yongluan Yan, Danyang Huang, Hao Liu, Yadong Li
• Abstract summary: We propose a vehicle attribute-guided method to re-rank vehicle Re-ID result. The attributes used include vehicle orientation and vehicle brand. Our method achieves mAP of 63.73% and rank-1 accuracy 76.61% in the CVPR 2021 AI City Challenge.
• Score: 7.028589578216994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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