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Title	Review Of Research On Vehicle Re-Identification Methods With Unsupervised Learning
ID_Doc	46471
Authors	Xu Y.; Guo X.; Rong L.
Year	2023
Published	Journal of Frontiers of Computer Science and Technology, 17, 5
DOI	http://dx.doi.org/10.3778/j.issn.1673-9418.2209100
Abstract	As one of the key technologies of intelligent transportation systems, vehicle re-identification (Re-ID) aims to retrieve the same vehicle from different monitoring scenes and plays an important role in building a safe and smart city. With the continuous development of computer vision, the Re-ID method of using supervised learning suffers from the problems of strong reliance on manual annotation in the training process and weak scene generalization ability, so unsupervised learning of vehicle Re-ID gradually becomes the focus of research in recent years. Firstly, the present mainstream vehicle Re-ID datasets and the commonly used model evaluation metrics are introduced. Then, latest unsupervised learning-based vehicle Re-ID methods are grouped into two categories: generative adversarial networks and clustering algorithms according to the current research ideas. Starting from the problems of domain deviation, cross-view deviation and insufficient information of data samples, the former is further divided into three categories of style transfer, multi-view generation, and data augmentation. For the labeling pro-blem, the latter can be divided into two categories of pseudo-labeled unsupervised domain adaptation and no label information required. With problem solving as the starting point, the fundamentals, advantages and disadvantages, and performance results of each type of method on mainstream datasets are summarized. Finally, the challenges faced by the current unsupervised learning for vehicle Re-ID are analyzed, and the future work in this research direction is prospected. © 2023, Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
Author Keywords	clustering; generative adversarial networks; intelligent transportation; unsupervised learning; vehicle re-identification

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