  Person re-identification (Re-ID) aims at retrieving a person of interest
across multiple non-overlapping cameras. With the advancement of deep neural
networks and increasing demand of intelligent video surveillance, it has gained
significantly increased interest in the computer vision community. By
dissecting the involved components in developing a person Re-ID system, we
categorize it into the closed-world and open-world settings. The widely studied
closed-world setting is usually applied under various research-oriented
assumptions, and has achieved inspiring success using deep learning techniques
on a number of datasets. We first conduct a comprehensive overview with
in-depth analysis for closed-world person Re-ID from three different
perspectives, including deep feature representation learning, deep metric
learning and ranking optimization. With the performance saturation under
closed-world setting, the research focus for person Re-ID has recently shifted
to the open-world setting, facing more challenging issues. This setting is
closer to practical applications under specific scenarios. We summarize the
open-world Re-ID in terms of five different aspects. By analyzing the
advantages of existing methods, we design a powerful AGW baseline, achieving
state-of-the-art or at least comparable performance on twelve datasets for FOUR
different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP)
for person Re-ID, indicating the cost for finding all the correct matches,
which provides an additional criteria to evaluate the Re-ID system for real
applications. Finally, some important yet under-investigated open issues are
discussed.
