  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.
