Regional attention selection and feature reinforcement for occluded person re-identification

ZHUANG Xuyao; WEI Dan; LIANG Danyang

doi:10.12299/jsues.23-0260

Volume 39 Issue 1

May 2025

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ZHUANG Xuyao, WEI Dan, LIANG Danyang. Regional attention selection and feature reinforcement for occluded person re-identification[J]. Journal of Shanghai University of Engineering Science, 2025, 39(1): 58-64. doi: 10.12299/jsues.23-0260

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ZHUANG Xuyao, WEI Dan, LIANG Danyang. Regional attention selection and feature reinforcement for occluded person re-identification[J]. Journal of Shanghai University of Engineering Science, 2025, 39(1): 58-64. doi: 10.12299/jsues.23-0260

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Regional attention selection and feature reinforcement for occluded person re-identification

doi: 10.12299/jsues.23-0260

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2023-12-15
Publish Date: 2025-05-19

Abstract

Abstract

Occluded person re-identification (ReID) in practical applications faces the main challenges of incomplete or noisy pedestrian features during the extraction and matching processes, which necessitates models with higher stability. Deep learning techniques were employed to construct the ReID model, which incorporates two modules: region attention selection and feature enhancement. The former adaptively selects regions of interest from the original image, while the latter distinguishes human features and applies weighting during the matching process. The ablation analysis demonstrates that these two modules are embeddable and beneficial to occluded pedestrian re-identification task. It can achieve Rank-1 accuracy rates of 55.9% and 79.1% on the Occluded-Duke and Occluded-ReID datasets, respectively.
- deep learning,
- neural networks,
- person re-identification (ReID),
- feature processing

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References(17)

References

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