@Article{jnm.2021.018027,
AUTHOR = {Zhiyuan Luo},
TITLE = {Review of GAN-Based Person Re-Identification},
JOURNAL = {Journal of New Media},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {11--17},
URL = {http://www.techscience.com/JNM/v3n1/41730},
ISSN = {2579-0129},
ABSTRACT = {Person re-ID is becoming increasingly popular in the field of modern 
surveillance. The purpose of person re-ID is to retrieve person of interests in 
non-overlapping multi-camera surveillance system. Due to the complexity of the 
surveillance scene, the person images captured by cameras often have problems 
such as size variation, rotation, occlusion, illumination difference, etc., which 
brings great challenges to the study of person re-ID. In recent years, studies 
based on deep learning have achieved great success in person re-ID. The 
improvement of basic networks and a large number of studies on the influencing 
factors have greatly improved the accuracy of person re-ID. Recently, some 
studies utilize GAN to tackle the domain adaptation task by transferring person 
images of source domain to the style of target domain and have achieved state of 
the art result in person re-ID.},
DOI = {10.32604/jnm.2021.018027}
}