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：基于视频的行人重识别（video re-ID）因其在监控、智慧城市和公共安全等领域的广泛应用，近来受到越来越多的关注。然而，由于视角变化、遮挡、姿态变化以及视频序列不确定性等诸多挑战，视频重识别任务难度较大，目前仍处于持续发展阶段。过去几年中，基于深度学习的视频重识别方法在公开数据集上不断取得突破性成果，各类技术方案被提出以应对视频重识别中的多样化问题。与基于图像的重识别相比，视频重识别更具挑战性和复杂性。为促进未来研究和技术攻关，本文首次系统综述了视频重识别深度学习方法的最新进展，主要涵盖三个重要方面：现有视频重识别方法及其局限性、重要技术里程碑与挑战、以及架构设计。本文提供了多种可用数据集的对比性能分析，提出了改进视频重识别的指导性建议与建设性思路，并展望了富有前景的研究方向。