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Unsupervised Domain Adaptation for Cross-Regional Scenes Person Re-Identification
Received date: 2023-12-19
Revised date: 2024-01-31
Accepted date: 2024-03-25
Online published: 2024-03-30
In large-scale surveillance systems, the lack of positive cross-camera pedestrian samples in cross-regional scenes limits the performance of person re-identification (Re-ID) models. To tackle this challenge, an unsupervised domain adaptive person re-identification method incorporating multi-granularity feature mining and domain-invariant feature learning is proposed. The method consists of a multi-granularity feature learning module and a domain distribution alignment module. Within the multi-granularity feature learning module, global discriminant features of pedestrians are extracted through global features learning. To further enhance the discriminative capability of pedestrian features, a local consistency feature learning module is proposed to strengthen the interactions among local features. By jointly learning global and local features, the network is encouraged to extract multi-granularity discriminative features, thereby improving the performance of the person re-identification model. Additionally, a domain distribution alignment module is incorporated, leveraging style transfer to generate positive samples with diverse styles across cameras for target domain. This not only addresses the issue of the lack of positive samples across cameras in cross-regional scenes but also enhances the domain adaptation capabilities of the model. Extensive experiments conducted on the Market-1501, DukeMTMC, CUHK03, and MSMT17 datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art person re-identification methods.
MAO Yanmei , LI Huafeng , ZHANG Yafei . Unsupervised Domain Adaptation for Cross-Regional Scenes Person Re-Identification[J]. Journal of Shanghai Jiaotong University, 2025 , 59(12) : 1878 -1890 . DOI: 10.16183/j.cnki.jsjtu.2023.635
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