TY - JOUR
T1 - Deep learning for visible-infrared cross-modality person re-identification
T2 - A comprehensive review
AU - Huang, Nianchang
AU - Liu, Jianan
AU - Miao, Yunqi
AU - Zhang, Qiang
AU - Han, Jungong
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China under Grant No. 61773301 . It is also supported by the Shaanxi Innovation Team Project under Grant No. 2018TD-012 and the Fundamental Research Funds for the Central Universities, China and the Innovation Fund of Xidian University, China.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Visible-infrared cross-modality person re-identification (VI-ReID) is currently a prevalent but challenging research topic in computer vision, since it can remedy the poor performance of existing single-modality ReID models under insufficient illumination, thus enabling the 24/7 surveillance systems. Although extensive research efforts have been dedicated to VI-ReID, a systematic and comprehensive literature review is still missing. Considering that, in this paper, a comprehensive review of VI-ReID approaches is provided. First, we clarify the importance, definition and challenges of VI-ReID. Secondly and most importantly, we elaborately analyze the motivations and the methodologies of existing VI-ReID methods. Accordingly, we will provide a comprehensive taxonomy, including 4 categories with 8 sub-items, for those state-of-the-art (SOTA) VI-ReID models. After that, we elaborate on some widely used datasets and evaluation metrics. Next, comprehensive comparisons of SOTA methods are made on the benchmark datasets. Based on the results, we point out the limitations of current methods. At last, we outline the challenges in this field and future research trends.
AB - Visible-infrared cross-modality person re-identification (VI-ReID) is currently a prevalent but challenging research topic in computer vision, since it can remedy the poor performance of existing single-modality ReID models under insufficient illumination, thus enabling the 24/7 surveillance systems. Although extensive research efforts have been dedicated to VI-ReID, a systematic and comprehensive literature review is still missing. Considering that, in this paper, a comprehensive review of VI-ReID approaches is provided. First, we clarify the importance, definition and challenges of VI-ReID. Secondly and most importantly, we elaborately analyze the motivations and the methodologies of existing VI-ReID methods. Accordingly, we will provide a comprehensive taxonomy, including 4 categories with 8 sub-items, for those state-of-the-art (SOTA) VI-ReID models. After that, we elaborate on some widely used datasets and evaluation metrics. Next, comprehensive comparisons of SOTA methods are made on the benchmark datasets. Based on the results, we point out the limitations of current methods. At last, we outline the challenges in this field and future research trends.
KW - Cross-modality person re-identification
KW - Deep learning
KW - Literature survey
KW - Evaluation metric
KW - NETWORK
KW - AUGMENTATION
KW - ALIGNMENT
KW - COVID-19
U2 - 10.1016/j.inffus.2022.10.024
DO - 10.1016/j.inffus.2022.10.024
M3 - Article
SN - 1566-2535
VL - 91
SP - 396
EP - 411
JO - Information Fusion
JF - Information Fusion
ER -