Comparison on unsupervised person re-identification: methods and experiments

Yanxin Xiang

doi:10.1117/12.2623122

1 December 2021 Comparison on unsupervised person re-identification: methods and experiments

Yanxin Xiang

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120791D (2021) https://doi.org/10.1117/12.2623122
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

Many existing methods have achieved the efficiency and speed of person re-identification deep neural models. However, due to the expensive cost of annotations, semi-supervised and unsupervised models have been treating seriously nowadays. The normal process of the unsupervised re-id model can be sum up into five steps and categorized into purely unsupervised learning person re-ID and unsupervised domain adaptation person re-ID. This paper will discuss some extant unsupervised re-id methods and address some existing problems about Person Re-identification. We also sort out the code of each paper mentioned and compare their performance on the existed dataset.

Citation Download Citation

Yanxin Xiang "Comparison on unsupervised person re-identification: methods and experiments", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120791D (1 December 2021); https://doi.org/10.1117/12.2623122

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