A survey of person re-identification based on deep learning

LI Qing; HU Wei-yang; LI Jiang-yun; LIU Yan; LI Meng-xuan

doi:10.13374/j.issn2095-9389.2020.12.22.004

Volume 44 Issue 5

May 2022

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Article Navigation > Chinese Journal of Engineering > 2022 > 44(5): 920-932

LI Qing, HU Wei-yang, LI Jiang-yun, LIU Yan, LI Meng-xuan. A survey of person re-identification based on deep learning[J]. Chinese Journal of Engineering, 2022, 44(5): 920-932. doi: 10.13374/j.issn2095-9389.2020.12.22.004

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LI Qing, HU Wei-yang, LI Jiang-yun, LIU Yan, LI Meng-xuan. A survey of person re-identification based on deep learning[J]. Chinese Journal of Engineering, 2022, 44(5): 920-932. doi: 10.13374/j.issn2095-9389.2020.12.22.004

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A survey of person re-identification based on deep learning

doi: 10.13374/j.issn2095-9389.2020.12.22.004

LI Qing^{1, 2, 3},
HU Wei-yang^{1, 2},
LI Jiang-yun^{1, 2, 3
,
,},
LIU Yan^{1, 2},
LI Meng-xuan^{1, 2}

1.
School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
3.
Shunde Graduate School, University of Science and Technology Beijing, Foshan 528000, China

More Information

Corresponding author: E-mail: leejy@ustb.edu.cn
Received Date: 2020-12-22
Available Online: 2021-11-24
Publish Date: 2022-05-25

Abstract

Abstract

Person re-identification is an important part of multi-target tracking across cameras; its aim is to identify the same person across different cameras. Given a query image, the purpose of person re-identification is to find the best match for the query image in an image set. Person re-identification is a key component in an intelligent security system; it is beneficial for building a smart bank or smart factory and plays a crucial role in the construction of a smart city. Nowadays, with the development of artificial intelligence and increasing demand for precise identification in practical scenarios, deep learning-based person re-identification technology has become a popular research topic; this technology has achieved state-of-the-art results in comparison with conventional approaches. Although there are many recently proposed networks with stronger representation ability and a high level of accuracy for person re-identification, there also exist some problems that should be considered and solved. These include the insufficient generalization ability of various poses, the inability to fully utilize the temporal information, and the ineffective identification of occluded objects. As a result, many scholars have researched this field and have pointed out some promising solutions to cope with the aforementioned problems. This paper aims to summarize the application of deep learning in the field of person re-identification along with its advantages and shortcomings. First, the background of person re-identification is introduced, including the application scenarios, datasets, and evaluation indicators. Additionally, some basic methods of person re-identification based on deep learning are summarized. According to the existing research on person re-identification, the main approaches proposed by scholars worldwide can be summarized into four aspects, which are based on local features, generative adversarial networks, video data, and re-ranking. A detailed comparative study of these four methods is then conducted. Finally, the existing problems and future studies that can be done in the field of person re-identification are analyzed and discussed.
- deep learning,
- person re-identification,
- local feature,
- generating adversarial networks,
- video data,
- reranking

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References(95)

References

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