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.

翻译：车辆再识别旨在从时间和摄像角度辨别特定车辆。随着智能运输系统和智能城市的迅速发展,车辆再识别技术越来越受到关注。然而,由于射击角度的不同和属于同一品牌的车辆高度相似性,车辆再识别对现有方法构成巨大挑战。在本文件中,我们提出车辆再识别结果重新排位的车辆属性指导方法。所使用的属性包括车辆方向和车辆品牌。我们还侧重于相机信息,并引入相机相互排斥理论,以进一步微调搜索结果。在特征提取方面,我们将多分辨率的数据增加与大型模型组合结合起来,以获得更稳健的车辆特征。我们的方法在CVPR 2021 AI City挑战中达到67.73%和1级准确度76.61%。