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Unsupervised domain adaptive person re-identification via camera penalty learning

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Abstract

Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to adapt the model trained on a labeled source domain to an unlabeled target domain. For pseudo-label-based UDA methods, pseudo label noise is the main problem for model degradation and cross-camera problem is one main factor to cause this noise. In this paper, a novel camera penalty learning (CPL) UDA person re-ID method is proposed to address this problem. The possibility of selecting wrong negative sample and positive sample is relatively high in conventional triplet loss due to cross-camera problem. To alleviate this problem, a camera-penalty-based triplet loss (PTL) is designed. It adds camera-ID-penalty to conventional triplet loss to reduce sample distance imbalance, thereby improving the quality of pseudo labels. In order to reduce the dependence on pseudo labels and improve the robustness, a camera-penalty-neighborhood loss (PNL) is designed and combined with the push loss (PL). The PNL minimizes the distance between one image and its camera-penalty-weighted neighbors. The PL maximizes the distances among all images. The proposed CPL model achieves considerable results of 87.4%/70.7% and 75.2%/59.0% Rank-1/ mAP on DukeMTMC-reID-to-Market-1501 and Market-1501-to-DukeMTMC-reID UDA tasks.

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Data availability

The datasets are a publicly available datasets.

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Acknowledgments

The work was supported in part by the National Nature Science Foundation of China [grant number 61872030] and Major Science and Technology Innovation Project of Shandong Province [grant number 2019TSLH0206].

Code availability

When accepted, the code can be publicly available.

Funding

This study was funded by the National Nature Science Foundation of China [grant number 61872030] and Major Science and Technology Innovation Project of Shandong Province [grant number 2019TSLH0206].

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Xiaodi Zhu, Yanfeng Li, Jia Sun, Houjin Chen & Jinlei Zhu

  2. Synthesis Electronic Technology Co., Ltd., Jinan, China

    Jinlei Zhu

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  1. Xiaodi Zhu
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  2. Yanfeng Li
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  3. Jia Sun
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  5. Jinlei Zhu
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Correspondence to Yanfeng Li.

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Conflict of interest

Yanfeng Li has received research grants from National Nature Science Foundation of China. Houjin Chen has research grants from Major Science and Technology Innovation Project of Shandong Province.

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Zhu, X., Li, Y., Sun, J. et al. Unsupervised domain adaptive person re-identification via camera penalty learning. Multimed Tools Appl 80, 15215–15232 (2021). https://doi.org/10.1007/s11042-021-10589-6

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  • DOI: https://doi.org/10.1007/s11042-021-10589-6

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