  Person re-identification (Re-ID) is one of the primary components of an
automated visual surveillance system. It aims to automatically identify/search
persons in a multi-camera network having non-overlapping field-of-views. Owing
to its potential in various applications and research significance, a plethora
of deep learning based re-Id approaches have been proposed in the recent years.
However, there exist several vision related challenges, e.g., occlusion, pose
scale \& viewpoint variance, background clutter, person misalignment and
cross-domain generalization across camera modalities, which makes the problem
of re-Id still far from being solved. Majority of the proposed approaches
directly or indirectly aim to solve one or multiple of these existing
challenges. In this context, a comprehensive review of current re-ID approaches
in solving theses challenges is needed to analyze and focus on particular
aspects for further advancements. At present, such a focused review does not
exist and henceforth in this paper, we have presented a systematic
challenge-specific literature survey of 230+ papers between the years of
2015-21. For the first time a survey of this type have been presented where the
person re-Id approaches are reviewed in such solution-oriented perspective.
Moreover, we have presented several diversified prominent developing trends in
the respective research domain which will provide a visionary perspective
regarding ongoing person re-Id research and eventually help to develop
practical real world solutions.
