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Title	A Comprehensive Review Of Pedestrian Re-Identification Based On Deep Learning
ID_Doc	925
Authors	Sun Z.; Wang X.; Zhang Y.; Song Y.; Zhao J.; Xu J.; Yan W.; Lv C.
Year	2024
Published	Complex and Intelligent Systems, 10, 2
DOI	http://dx.doi.org/10.1007/s40747-023-01229-7
Abstract	Pedestrian re-identification (re-ID) has gained considerable attention as a challenging research area in smart cities. Its applications span diverse domains, including intelligent transportation, public security, new retail, and the integration of face re-ID technology. The rapid progress in deep learning techniques, coupled with the availability of large-scale pedestrian datasets, has led to remarkable advancements in pedestrian re-ID. In this paper, we begin the study by summarising the key datasets and standard evaluation methodologies for pedestrian re-ID. Second, we look into pedestrian re-ID methods that are based on object re-ID, loss functions, research directions, weakly supervised classification, and various application scenarios. Moreover, we assess and display different re-ID approaches from deep learning perspectives. Finally, several challenges and future directions for pedestrian re-ID development are discussed. By providing a holistic perspective on this topic, this research serves as a valuable resource for researchers and practitioners, enabling further advancements in pedestrian re-ID within smart city environments. © The Author(s) 2023.
Author Keywords	Deep learning; Large-scale datasets; Pedestrian re-identification; Smart cities

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