Person re-identification (re-id) addresses the problem of whether “a query image corresponds to an identity in the database” and is believed to play a fundamental role in security enforcement in the near future, particularly in crowded urban environments. Due to many possibilities in selecting appropriate model architectures, datasets, and settings, the performance reported by the state-of-the-art re-id methods oscillates significantly among the published surveys. Therefore, it is difficult to understand the mainstream trends and emerging research difficulties in person re-id. This paper proposes a multi-dimensional taxonomy to categorize the most relevant researches according to different perspectives and tries to unify the categorization of re-id methods and fill the gap between the recently published surveys. Furthermore, we discuss the open challenges with a focus on privacy concerns and the issues caused by the exponential increase in the number of re-id publications over the recent years. Finally, we discuss several challenging directions for future studies.

人员重新识别（re-id）解决了“查询图像是否对应于数据库中的身份”的问题”，并且据信在不久的将来，特别是在拥挤的城市环境中，它将在安全执法中发挥重要作用。由于选择合适的模型体系结构，数据集和设置的可能性很大，因此，最新的re-id方法报告的性能在已发布的调查中明显波动。因此，很难理解人们重新认识的主流趋势和新兴的研究困难。本文提出了一个多维分类法，根据不同的观点对最相关的研究进行分类，并试图统一re-id方法的分类，以填补最近发表的调查之间的空白。此外，我们讨论开放性挑战，重点关注隐私问题以及近年来re-id出版物数量呈指数增长所引起的问题。最后，我们讨论了未来研究的几个具有挑战性的方向。