Deep Learning for Video-based Person Re-Identification: A Survey

TL;DR

This survey reviews recent deep learning advancements in video-based person re-identification, highlighting challenges, methodologies, and performance analysis to guide future research in this complex field.

Contribution

It provides the first comprehensive overview of deep learning techniques for video re-ID, including methods, challenges, and architectural insights, along with performance comparisons.

Findings

Deep learning has significantly improved video re-ID accuracy.

Handling occlusion and pose variation remains challenging.

Performance varies across datasets and methods.

Abstract

Video-based person re-identification (video re-ID) has lately fascinated growing attention due to its broad practical applications in various areas, such as surveillance, smart city, and public safety. Nevertheless, video re-ID is quite difficult and is an ongoing stage due to numerous uncertain challenges such as viewpoint, occlusion, pose variation, and uncertain video sequence, etc. In the last couple of years, deep learning on video re-ID has continuously achieved surprising results on public datasets, with various approaches being developed to handle diverse problems in video re-ID. Compared to image-based re-ID, video re-ID is much more challenging and complex. To encourage future research and challenges, this first comprehensive paper introduces a review of up-to-date advancements in deep learning approaches for video re-ID. It broadly covers three important aspects, including brief video re-ID methods with their limitations, major milestones with technical challenges, and architectural design. It offers comparative performance analysis on various available datasets, guidance to improve video re-ID with valuable thoughts, and exciting research directions.