  Person re-identification has received a lot of attention from the research
community in recent times. Due to its vital role in security based
applications, person re-identification lies at the heart of research relevant
to tracking robberies, preventing terrorist attacks and other security critical
events. While the last decade has seen tremendous growth in re-id approaches,
very little review literature exists to comprehend and summarize this progress.
This review deals with the latest state-of-the-art deep learning based
approaches for person re-identification. While the few existing re-id review
works have analysed re-id techniques from a singular aspect, this review
evaluates numerous re-id techniques from multiple deep learning aspects such as
deep architecture types, common Re-Id challenges (variation in pose, lightning,
view, scale, partial or complete occlusion, background clutter), multi-modal
Re-Id, cross-domain Re-Id challenges, metric learning approaches and video
Re-Id contributions. This review also includes several re-id benchmarks
collected over the years, describing their characteristics, specifications and
top re-id results obtained on them. The inclusion of the latest deep re-id
works makes this a significant contribution to the re-id literature. Lastly,
the conclusion and future directions are included.
