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.
Abstract (translated)
视频身份识别(视频重配)最近吸引了越来越多的关注,因为它在许多领域都具有广泛的应用,例如监控、智慧城市和公共安全等。然而,视频重配仍然是一项相当困难的任务,并且仍然是一个持续的阶段,因为有许多不确定的挑战,例如视角、遮挡、姿态变化和不确定的视频序列等。在过去两年中,深度学习在视频重配方面已经取得了令人惊奇的结果,在公开数据集上开发了各种方法来处理各种视频重配问题。与基于图像的身份识别相比,视频重配更具挑战性和复杂性。为了鼓励未来的研究和挑战,本综述性论文介绍了关于视频重配深度学习方法的最新进展。它涵盖了三个重要的方面,包括简短的视频重配方法及其限制、具有技术挑战的主要里程碑和建筑设计。它提供了对各种可用数据集的比较性能分析、有价值的思想和改进视频重配的方法,并提出了令人兴奋的研究方向。
URL
https://arxiv.org/abs/2303.11332
