Person Re-Identification using Deep Learning Networks: A Systematic Review

TL;DR

This paper systematically reviews recent deep learning approaches for person re-identification, covering various architectures, challenges, and benchmarks, providing a comprehensive overview of the state-of-the-art in this security-critical field.

Contribution

It offers a comprehensive evaluation of multiple deep learning techniques for person re-identification, including challenges, multi-modal and cross-domain issues, and benchmark analyses, which was lacking in prior reviews.

Findings

Deep learning architectures significantly improve re-id accuracy.

Challenges like pose variation and occlusion remain difficult.

Benchmark datasets show steady progress in re-id performance.

Abstract

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.