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 commence by offering a meticulous overview of the datasets and evaluation criteria utilized in the realm of occluded person Re-ID. Subsequently, we undertake a rigorous scientific classification and analysis of existing deep learning-based occluded person Re-ID methodologies, examining them from diverse perspectives and presenting concise summaries for each approach. Furthermore, we execute a systematic comparative analysis among these methods, pinpointing the state-of-the-art solutions, and provide insights into the future trajectory of occluded person Re-ID research. © 2023 Elsevier Ltd