Multi-Domain Learning and Identity Mining for Vehicle Re-Identification

• URL: http://arxiv.org/abs/2004.10547v2
• Date: Fri, 24 Apr 2020 14:08:35 GMT
• Title: Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
• Authors: Shuting He, Hao Luo, Weihua Chen, Miao Zhang, Yuqi Zhang, Fan Wang, Hao Li and Wei Jiang
• Abstract summary: This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20) The Track2 is a vehicle re-identification task with both the real-world data and synthetic data. With multiple-model ensemble, our method achieves 0.7322 in the mAP score which yields third place in the competition.
• Score: 38.35753364518881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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