Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent surveillance systems. Widespread occlusion significantly impacts the performance of person Re-ID. Occluded person Re-ID refers to a pedestrian matching method that deals with challenges such as pedestrian information loss, noise interference, and perspective misalignment. It has garnered extensive attention from researchers. Over the past few years, several occlusion-solving person Re-ID methods have been proposed, tackling various sub-problems arising from occlusion. However, there is a lack of comprehensive studies that compare, summarize, and evaluate the potential of occluded person Re-ID methods in detail. In this review, we start by providing a detailed overview of the datasets and evaluation scheme used for occluded person Re-ID. Next, we scientifically classify and analyze existing deep learning-based occluded person Re-ID methods from various perspectives, summarizing them concisely. Furthermore, we conduct a systematic comparison among these methods, identify the state-of-the-art approaches, and present an outlook on the future development of occluded person Re-ID.

翻译：行人重识别技术在智能监控系统中发挥着越来越重要的作用。广泛存在的遮挡现象严重影响了行人重识别的性能。遮挡行人重识别是指一种应对行人信息丢失、噪声干扰和视角错位等挑战的行人匹配方法，已引起研究者的广泛关注。近年来，研究者提出了多种解决遮挡问题的行人重识别方法，以应对遮挡带来的各种子问题。然而，目前尚缺乏全面比较、总结和评估遮挡行人重识别方法潜力的系统性研究。在本综述中，我们首先详细介绍了遮挡行人重识别的数据集和评估方案。接着，我们从多个角度对现有基于深度学习的遮挡行人重识别方法进行科学分类与分析，并对其进行了简明总结。此外，我们对这些方法进行了系统比较，识别出当前最先进的方法，并对遮挡行人重识别的未来发展进行了展望。