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Visible-Infrared Person Re-Identification: A Comprehensive Survey and a New Setting

Electronics (Basel), 2022-02, Vol.11 (3), p.454 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2079-9292 ;EISSN: 2079-9292 ;DOI: 10.3390/electronics11030454

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  • Title:
    Visible-Infrared Person Re-Identification: A Comprehensive Survey and a New Setting
  • Author: Zheng, Huantao ; Zhong, Xian ; Huang, Wenxin ; Jiang, Kui ; Liu, Wenxuan ; Wang, Zheng
  • Subjects: Algorithms ; Cameras ; Datasets ; generative-based model ; Infrared imagery ; literature survey ; Machine learning ; non-generative-based model ; Performance evaluation ; State-of-the-art reviews ; Surveillance ; visible-infrared person re-identification
  • Is Part Of: Electronics (Basel), 2022-02, Vol.11 (3), p.454
  • Description: Person re-identification (ReID) plays a crucial role in video surveillance with the aim to search a specific person across disjoint cameras, and it has progressed notably in recent years. However, visible cameras may not be able to record enough information about the pedestrian’s appearance under the condition of low illumination. On the contrary, thermal infrared images can significantly mitigate this issue. To this end, combining visible images with infrared images is a natural trend, and are considerably heterogeneous modalities. Some attempts have recently been contributed to visible-infrared person re-identification (VI-ReID). This paper provides a complete overview of current VI-ReID approaches that employ deep learning algorithms. To align with the practical application scenarios, we first propose a new testing setting and systematically evaluate state-of-the-art methods based on our new setting. Then, we compare ReID with VI-ReID in three aspects, including data composition, challenges, and performance. According to the summary of previous work, we classify the existing methods into two categories. Additionally, we elaborate on frequently used datasets and metrics for performance evaluation. We give insights on the historical development and conclude the limitations of off-the-shelf methods. We finally discuss the future directions of VI-ReID that the community should further address.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2079-9292
    EISSN: 2079-9292
    DOI: 10.3390/electronics11030454
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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