车辆重新识别的坚实基线 (A Strong Baseline for Vehicle Re-Identification)

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://github.com/cybercore-co-ltd/track2_aicity_2021.

翻译：车辆再识别(Re-ID)旨在识别不同相机的同一车辆,从而在现代交通管理系统中发挥重要作用。技术挑战要求各种观点、分辨率、封闭度和照明性条件的算法必须稳健。本文首先分析阻碍车辆再识别性能的主要因素。然后我们提出解决方案,具体针对第五次AI城市挑战的数据集第2轨,包括(1) 缩小真实数据和合成数据之间的域间差距,(2) 通过堆叠多位头和关注机制对网络进行修改,(3) 调整适应性损失重量。我们的方法在不使用外部数据集或假标签的情况下,实现了61.34%的市花样测试仪,并超越了Veri基准上87.1% mAP的所有以往工作。该代码可在https://github.com/cybercore-co-ltd/tract2_aicity_2021查阅。