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Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, Steven C. H. Hoi
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.
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Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., & Hoi, S. C. H. (2021). Deep Learning for Person Re-Identification: A Survey and Outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(6), 2872–2893. https://doi.org/10.1109/tpami.2021.3054775
Ye, Mang, et al. "Deep Learning for Person Re-Identification: A Survey and Outlook." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, 2021, pp. 2872–2893, https://doi.org/10.1109/tpami.2021.3054775.
Ye, Mang, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven C. H. Hoi. "Deep Learning for Person Re-Identification: A Survey and Outlook." IEEE Transactions on Pattern Analysis and Machine Intelligence 44, no. 6 (2021): 2872–2893. https://doi.org/10.1109/tpami.2021.3054775.
Ye M., Shen J., Lin G. [и др.] Deep Learning for Person Re-Identification: A Survey and Outlook // IEEE Transactions on Pattern Analysis and Machine Intelligence. — 2021. — Vol. 44, № 6. — P. 2872–2893. — URL: https://doi.org/10.1109/tpami.2021.3054775
@article{ye2021deep,
title = {{Deep Learning for Person Re-Identification: A Survey and Outlook}},
author = {Mang Ye and Jianbing Shen and Gaojie Lin and Tao Xiang and Ling Shao and Steven C. H. Hoi},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2021},
volume = {44},
number = {6},
pages = {2872--2893},
doi = {10.1109/tpami.2021.3054775},
url = {https://doi.org/10.1109/tpami.2021.3054775}
}TY - JOUR AU - Ye, Mang AU - Shen, Jianbing AU - Lin, Gaojie AU - Xiang, Tao AU - Shao, Ling AU - Hoi, Steven C. H. TI - Deep Learning for Person Re-Identification: A Survey and Outlook JO - IEEE Transactions on Pattern Analysis and Machine Intelligence VL - 44 IS - 6 SP - 2872 EP - 2893 PY - 2021 DA - 2021/01/26 DO - 10.1109/tpami.2021.3054775 UR - https://doi.org/10.1109/tpami.2021.3054775 ER -
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