GAN-based unsupervised domain adaptive person re-identification

Shengsheng ZHENG; Haibing YIN; Xiaofeng HUANG; Tianjie ZHANG

doi:10.11959/j.issn.1000-0801.2021016

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GAN-based unsupervised domain adaptive person re-identification

Research and Development | 更新时间：2024-06-05

- GAN-based unsupervised domain adaptive person re-identification
- Telecommunications Science Vol. 37, Issue 2, Pages: 99-106(2021)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2021016
  CLC： TP391
- Published Online：2021-02，
  
  Published：20 February 2021
- 稿件说明：
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Shengsheng ZHENG, Haibing YIN, Xiaofeng HUANG, et al. GAN-based unsupervised domain adaptive person re-identification[J]. Telecommunications science, 2021, 37(2): 99-106.
DOI：

Shengsheng ZHENG, Haibing YIN, Xiaofeng HUANG, et al. GAN-based unsupervised domain adaptive person re-identification[J]. Telecommunications science, 2021, 37(2): 99-106. DOI： 10.11959/j.issn.1000-0801.2021016.

摘要

针对无监督域自适应行人重识别中存在的聚类不准确导致网络识别准确率低的问题，提出一种基于生成对抗网络的无监督域自适应行人重识别方法。首先通过在池化层后使用批量归一化层、删除一层全连接层和使用Adam优化器等方法优化CNN模型；然后基于最小错误率贝叶斯决策理论分析聚类错误率和选择聚类关键参数；最后利用生成对抗网络调整聚类，有效提升了无监督域自适应行人重识别的识别准确率。在源域Market-1501和目标域DukeMTMC-reID下进行实验，mAP和Rank-1分别达到了53.7%和71.6%。

Abstract

Aiming at the problem that the inaccurate clustering in the unsupervised domain adaptive pedestrian re-recognition results in low network recognition accuracy

an unsupervised domain adaptive pedestrian re-recognition method based on generative confrontation network was proposed.Firstly

the CNN model was optimized by using the batch normalization layer after the pooling layer

deleting a fully connected layer and adopting the Adam optimizer.Secondly

the cluster error was analyzed and the important parameter in the cluster was decided based on minimum error rate Bayesian decision theory.Finally

the generative adversarial network was utilized to adjust the cluster.These steps effectively improved the recognition accuracy of unsupervised domain adaptive person re-identification.In the case of the source domain Market-1501 and the target domain DukeMTMC-reID

experimental results show that mAP and Rank-1 can reach 53.7% and 71.6%

respectively.

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references

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