Person re-identification (re-ID) has become increasingly popular in the
community due to its application and research significance. It aims at spotting
a person of interest in other cameras. In the early days, hand-crafted
algorithms and small-scale evaluation were predominantly reported. Recent years
have witnessed the emergence of large-scale datasets and deep learning systems
which make use of large data volumes. Considering different tasks, we classify
most current re-ID methods into two classes, i.e., image-based and video-based;
in both tasks, hand-crafted and deep learning systems will be reviewed.
Moreover, two new re-ID tasks which are much closer to real-world applications
are described and discussed, i.e., end-to-end re-ID and fast re-ID in very
large galleries. This paper: 1) introduces the history of person re-ID and its
relationship with image classification and instance retrieval; 2) surveys a
broad selection of the hand-crafted systems and the large-scale methods in both
image- and video-based re-ID; 3) describes critical future directions in
end-to-end re-ID and fast retrieval in large galleries; and 4) finally briefs
some important yet under-developed issues.

这篇文章介绍了人员再识别技术及其发展历史，对手工算法和深度学习算法在画面和视频再识别任务中的表现进行了概述，描述了未来方向和需要提高的问题。