Abstract
Person re-identification (ReId), a crucial task in surveillance, involves matching individuals across different camera views. The advent of Deep Learning, especially supervised techniques like Convolutional Neural Networks and Attention Mechanisms, has significantly enhanced person Re-ID. However, the success of supervised approaches hinges on vast amounts of annotated data, posing scalability challenges in data labeling and computational costs. To address these limitations, recent research has shifted towards unsupervised person re-identification. Leveraging abundant unlabeled data, unsupervised methods aim to overcome the need for pairwise labelled data. Although traditionally trailing behind supervised approaches, unsupervised techniques have shown promising developments in recent years, signalling a narrowing performance gap. Motivated by this evolving landscape, our survey pursues two primary objectives. First, we review and categorize significant publications in supervised person re-identification, providing an in-depth overview of the current state-of-the-art and emphasizing little room for further improvement in this domain. Second, we explore the latest advancements in unsupervised person re-identification over the past three years, offering insights into emerging trends and shedding light on the potential convergence of performance between supervised and unsupervised paradigms. This dual-focus survey aims to contribute to the evolving narrative of person re-identification, capturing both the mature landscape of supervised techniques and the promising outcomes in the realm of unsupervised learning.
Abstract (translated)
人员再识别（ReId）是监控领域的一项关键任务，它涉及在不同摄像头视角下匹配个体。随着深度学习的兴起，特别是卷积神经网络和注意力机制等监督技术的应用，人员再识别的效果得到了显著提升。然而，这些方法的成功很大程度上依赖于大量的标注数据，这对数据标签的可扩展性和计算成本提出了挑战。为了克服这些限制，最近的研究转向了无监督人员再识别领域。通过利用丰富的未标记数据，无监督方法旨在减少对成对标注数据的需求。尽管传统上落后于监督方法，近年来无监督技术已显示出令人鼓舞的发展，表明其与监督方法在性能上的差距正在缩小。 鉴于这一不断变化的格局，我们的综述追求两个主要目标。首先，我们回顾并分类了监督人员再识别领域的重大出版物，提供了当前最新技术水平的深入概述，并强调在这个领域进一步改进的空间有限。其次，我们探索了过去三年无监督人员再识别领域的最新进展，提供新兴趋势的见解，并阐明监督和无监督范式之间性能趋同的可能性。 这项双重焦点的综述旨在为不断发展的人员再识别叙事做出贡献，捕捉到监督技术成熟格局的同时，也关注无监督学习领域中充满希望的结果。
URL
https://arxiv.org/abs/2509.22690
