Unsupervised person Re-identification: A review of recent works

Abstract

Re-identification (Re-ID) is a process that seeks to identify concern individuals from successive non-overlapping photographs. The area of computer vision has recently seen an uptick in the amount of attention focused on deep neural networks, especially given the popularity of smart monitoring systems and the development of sophisticated learning algorithms. We classified existing Re-ID technologies into closed-world and open-world contexts based on the used components. The closed-world scenario has been commonly used under a variety of data analysis hypotheses, and it brought precise results when applied to a variety of datasets utilizing deep learning techniques. We began with a comprehensive overview of closed-world person Re-ID considering deep metric learning, an extensive representation of features learning, ranking optimization, and in-depth analysis. Due to the accomplishment of performance in the packed scenario, the Re-ID focuses research has lately turned to a bare environment setting, which brings new issues. This setting is more akin to what we'd find in real-world circumstances. We summarized the unsupervised Re-ID literature as well as current research trends and proposed future studies.