Comparison on Unsupervised Person Re-identification: Methods and Experiments

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.