Person Re-identification Method Based on Color Attack and Joint Defence

Gong, Yunpeng; Huang, Ling; Chen, Lifei

doi:10.1109/cvprw56347.2022.00477

Cited by 11 publications

(1 citation statement)

References 33 publications

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“…This method improved the feature consistency and robustness in cross-modal cases, thus enhancing the accuracy and performance of re-ID. Gong et al [170] tackled color transformation over-fitting in re-ID by fusing contour and color features using a local transformation attack method.…”

Section: Cross-modal Scenariosmentioning

confidence: 99%

A comprehensive review of pedestrian re-identification based on deep learning

Sun,

Wang,

Zhang

et al. 2023

Complex Intell. Syst.

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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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Section: Cross-modal Scenariosmentioning

confidence: 99%