In the last few years

with the development of deep learning theory and person re-identification (re-id) methods

re-id techniques has achieved great breakthrough and gained high recognition accuracy in constrained environments. However

the existing re-id approaches perform poor in unconstrained environments and are still far from practical applications. There are many significant challenges in unconstrained environments

including lack of training samples

dramatic illumination variations

person occlusion and open-set tests

which significantly decreases the performance of re-id models. In this paper

we will introduce the latest improvements

the involved datasets

the existing problems and the future trends of the unconstrained person re-identification techniques

especially for unsupervised re-id

visible-infrared re-id

occlusion re-id

and open-set re-id.