@Article{jnm.2021.023981,
AUTHOR = {Yang Dai, Zhiyuan Luo},
TITLE = {Review of Unsupervised Person Re-Identification},
JOURNAL = {Journal of New Media},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {129--136},
URL = {http://www.techscience.com/JNM/v3n4/45475},
ISSN = {2579-0129},
ABSTRACT = {Person re-identification (re-ID) aims to match images of the same 
pedestrian across different cameras. It plays an important role in the field of 
security and surveillance. Although it has been studied for many years, it is still 
considered as an unsolved problem. Since the rise of deep learning, the accuracy 
of supervised person re-ID on public datasets has reached the highest level. 
However, these methods are difficult to apply to real-life scenarios because a 
large number of labeled training data is required in this situation. Pedestrian 
identity labeling, especially cross-camera pedestrian identity labeling, is heavy 
and expensive. Why we cannot apply the pre-trained model directly to the 
unseen camera network? Due to the existence of domain bias between source and 
target environment, the accuracy on target dataset is always low. For example, 
the model trained on the mall needs to adapt to the new environment of airport 
obviously. Recently, some researches have been proposed to solve this problem, 
including clustering-based methods, GAN-based methods, co-training methods 
and unsupervised domain adaptation methods.},
DOI = {10.32604/jnm.2021.023981}
}