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Overview of person re-identification in unconstrained environments. (Chinese. English summary) Zbl 07266364
Summary: In the last few years, with the development of deep learning theory and person re-identification (re-id) methods, re-id techniques have achieved great breakthrough and gained high recognition accuracy in constrained environments. However, the existing re-id approaches perform poor in unconstrained environments and are still far from practical applications. There are many significant challenges in unconstrained environments, including lack of training samples, dramatic illumination variations, person occlusion and open-set tests, which significantly decrease the performance of re-id models. In this paper, we introduce the latest improvements, the involved datasets, the existing problems and the future trends of the unconstrained person re-identification techniques, especially for unsupervised re-id, visible-infrared re-id, occlusion re-id, and open-set re-id.
MSC:
68T10 Pattern recognition, speech recognition
68T45 Machine vision and scene understanding
68T05 Learning and adaptive systems in artificial intelligence
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