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Person re-identification (Re-ID) technology plays an increasingly crucialrole in intelligent surveillance systems. Widespread occlusion significantlyimpacts the performance of person Re-ID. Occluded person Re-ID refers to apedestrian matching method that deals with challenges such as pedestrianinformation loss, noise interference, and perspective misalignment. It hasgarnered extensive attention from researchers. Over the past few years, severalocclusion-solving person Re-ID methods have been proposed, tackling varioussub-problems arising from occlusion. However, there is a lack of comprehensivestudies that compare, summarize, and evaluate the potential of occluded personRe-ID methods in detail. In this review, we start by providing a detailedoverview of the datasets and evaluation scheme used for occluded person Re-ID.Next, we scientifically classify and analyze existing deep learning-basedoccluded person Re-ID methods from various perspectives, summarizing themconcisely. Furthermore, we conduct a systematic comparison among these methods,identify the state-of-the-art approaches, and present an outlook on the futuredevelopment of occluded person Re-ID.

Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives