Intelligent video-surveillance is currently an active research field in
computer vision and machine learning techniques. It provides useful tools for
surveillance operators and forensic video investigators. Person
re-identification (PReID) is one among these tools. It consists of recognizing
whether an individual has already been observed over a camera in a network or
not. This tool can also be employed in various possible applications such as
off-line retrieval of all the video-sequences showing an individual of interest
whose image is given a query, and online pedestrian tracking over multiple
camera views. To this aim, many techniques have been proposed to increase the
performance of PReID. Among the systems, many researchers utilized deep neural
networks (DNNs) because of their better performance and fast execution at test
time. Our objective is to provide for future researchers the work being done on
PReID to date. Therefore, we summarized state-of-the-art DNN models being used
for this task. A brief description of each model along with their evaluation on
a set of benchmark datasets is given. Finally, a detailed comparison is
provided among these models followed by some limitations that can work as
guidelines for future research.

通过对比基准数据集，研究了目前在智能视频监控和机器学习技术领域中，用于人员再识别的深度神经网络模型的表现及局限性，为未来的研究提供了基础。