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Sun, Zhaojie; Wang, Xuan; Zhang, Youlei; Song, Yongchao; Zhao, Jindong; Xu, Jindong; Yan, Weiqing; Lv, Cuicui
Complex & intelligent systems, 04/2024, Volume: 10, Issue: 2Journal Article
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.
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