Multi-Domain Learning and Identity Mining for Vehicle Re-Identification (2020-04-22T00:00:00.000000Z)

TL;DR

The Identity Mining method to automatically generate pseudo labels for a part of the testing data, which is better than the k-means clustering, is proposed and achieves third place in the AICITY20 competition.

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.

Authors

F. Wang

4 papers

Yuqi Zhang

2 papers

Shuting He

5 papers

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

TL;DR

Abstract

Authors

References31 items

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