This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID. At first, we propose a multi-domain learning method to joint the real-world and synthetic data to train the model. Then, we propose the Identity Mining method to automatically generate pseudo labels for a part of the testing data, which is better than the k-means clustering. The tracklet-level re-ranking strategy with weighted features is also used to post-process the results. Finally, with multiple-model ensemble, our method achieves 0.7322 in the mAP score which yields third place in the competition. The codes are available at https://github.com/heshuting555/AICITY2020_DMT_VehicleReID.

本文介绍了我们在 AICITY20 的 Track2 中的解决方案，该方案使用多域学习法联合真实世界和合成数据训练模型，并提出了 Identity Mining 方法自动生成部分测试数据的伪标签，同时使用轨迹级重新排序策略和多模型集成，模型在mAP分数上取得了0.7322的成绩，获得比赛的第三名。

车辆重新识别的多域学习和身份挖掘