A brief survey for person re-identification based on deep learning

Person Re-Identification (Re-ID) has been paid more attention due to its wide application in intelligent surveillance systems. Finding the same person from other non-overlapping cameras when a specific image of the pedestrian is given, which is a challenging problem for the reason of viewpoint variation, clothes changing, low resolution, etc. In this paper, we motivate reviewing for deep learning-based methods of Person Re-ID. We present a detailed survey of the state-of-the-art in terms of the description and analysis of supervised-based and unsupervised-based networks and their performance evaluation in the commonly used data sets. Finally, we analyse the challenging problems and discuss future works in this area.