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.

翻译：视频行人重识别（video re-ID）近年来因其在监控、智慧城市和公共安全等多个领域的广泛实际应用而日益受到关注。然而，由于存在视角、遮挡、姿态变化以及不确定的视频序列等众多未解决的挑战，视频行人重识别相当困难，且仍处于持续发展阶段。近几年来，基于深度学习的视频行人重识别方法在公开数据集上持续取得令人瞩目的成果，各类方法被开发出来以应对视频行人重识别中的多种问题。与基于图像的重识别相比，视频行人重识别更具挑战性和复杂性。为促进未来研究并应对挑战，这篇首篇综合性论文对视频行人重识别中深度学习方法的近期进展进行了综述。本文广泛涵盖三个重要方面，包括简要介绍视频行人重识别方法及其局限性、主要里程碑与技术挑战，以及架构设计。本文在多种可用数据集上提供了对比性能分析、改进视频行人重识别的指导意见与宝贵思路，以及令人兴奋的研究方向。