One of the indispensable pillars of a smart city is its surveillance infrastructure, and it requires smart techniques to analyze the videos acquired from the surveillance cameras. Person re-identification (PRId) is one of the fundamental tasks in automated visual surveillance, and it has been an area of extensive research spanning the past decade. PRId aims at finding a person who has previously been seen or identified using some unique descriptor of the person. This survey comprises a broad spectrum of PRId methods spanning from traditional to deep-learning, being analyzed and compared. This survey also discusses various PRId frameworks based on machine learning and deep learning. This study emphasizes the challenges in building PRId systems for the benefits of smart cities and presents a critical overview of recent progress and the state-of-the-art approaches to solving some significant challenges of existing PRId systems.

智能城市不可或缺的支柱之一是其监视基础设施，它需要智能技术来分析从监视摄像机获取的视频。人员重新识别（PRId）是自动视觉监视的基本任务之一，并且在过去十年中一直是广泛研究的领域。PRId旨在使用该人的某些唯一描述符来找到先前曾被看到或识别过的人。这项调查包括从传统到深度学习的广泛PRId方法，并进行了分析和比较。该调查还讨论了基于机器学习和深度学习的各种PRId框架。