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Survey for person re-identification based on coarse-to-fine feature learning

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Abstract

Person re-identification (Re-ID), aiming to retrieve interested people through multiple non-overlapping cameras, has caused concerns in pattern recognition communities and computer vision in recent years. With the continuous promotion of deep learning, the research on person Re-ID is more and more extensive. In this paper, we conduct a comprehensive review of the advanced methods and divide them into three categories from coarse to fine: (1) global-based methods, which are based on whole images to obtain discriminative features; (2) part-based methods, which focus on image regions to extract detailed information; (3) multiple granularities-based methods, which combine advantages of the above two categories. For each category, we further classify it according to popular research tools. Then, we give the evaluation of some typical models on a set of benchmark datasets and compare them in detail. We also introduce some widely used training tricks. The methods mentioned in this paper were published in 2011-2021. By discussing their advantages and limitations, we provide a reference for future works.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Minjie Liu, Jiaqi Zhao, Yong Zhou, Hancheng Zhu, Rui Yao & Ying Chen

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Minjie Liu, Jiaqi Zhao, Yong Zhou, Hancheng Zhu, Rui Yao & Ying Chen

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Author Minjie Liu, Author Jiaqi Zhao, Author Yong Zhou, Author Hancheng Zhu, Author Rui Yao and Author Ying Chen declare that they have no conflict of interest.

Author Jiaqi Zhao has received research grants No. 61806206 from the National Natural Science Foundation of China and No. BK20180639 from the Natural Science Foundation of Jiangsu Province.

Author Yong Zhou has received research grants No. BK20201346 from the Natural Science Foundation of Jiangsu Province and No. 2015-DZXX-010 from the Six Talent Peaks Project in Jiangsu Province.

Author Hancheng Zhu has received research grants No. 62101555 from the National Natural Science Foundation of China and No. BK20210488 from the Natural Science Foundation of Jiangsu Province.

Author Rui Yao has received research grants No. 62172417 from the National Natural Science Foundation of China and No. 2018-XYDXX-044 from the Six Talent Peaks Project in Jiangsu Province.

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Liu, M., Zhao, J., Zhou, Y. et al. Survey for person re-identification based on coarse-to-fine feature learning. Multimed Tools Appl 81, 21939–21973 (2022). https://doi.org/10.1007/s11042-022-12510-1

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