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Title	Viewpoint-Aware Progressive Clustering For Unsupervised Vehicle Re-Identification
ID_Doc	61122
Authors	Zheng A.; Sun X.; Li C.; Tang J.
Year	2022
Published	IEEE Transactions on Intelligent Transportation Systems, 23, 8
DOI	http://dx.doi.org/10.1109/TITS.2021.3103961
Abstract	Vehicle re-identification (Re-ID) is an active task due to its importance in large-scale intelligent monitoring in smart cities. Despite the rapid progress in recent years, most existing methods handle vehicle Re-ID task in a supervised manner, which is both time and labor-consuming and limits their application to real-life scenarios. Recently, unsupervised person Re-ID methods achieve impressive performance by exploring domain adaption or clustering-based techniques. However, one cannot directly generalize these methods to vehicle Re-ID since vehicle images present huge appearance variations in different viewpoints. To handle this problem, we propose a novel viewpoint-aware clustering algorithm for unsupervised vehicle Re-ID. In particular, we first divide the entire feature space into different subspaces according to the predicted viewpoints and then perform a progressive clustering to mine the accurate relationship among samples. Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi-776 and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID. © 2000-2011 IEEE.
Author Keywords	progressive clustering; unsupervised learning; vehicle Re-ID; Viewpoint-aware

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