Jointly adaptive cross-resolution person re-identification on super-resolution
Abstract Cross-resolution Person Re-identification (ReID) faces the significant challenge of large resolution variance across different camera views in real surveillance systems. Most approaches based on super-resolution (SR) excessively rely on the SR images, which may lead to the loss of low-resol...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-04-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01881-1 |
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| Summary: | Abstract Cross-resolution Person Re-identification (ReID) faces the significant challenge of large resolution variance across different camera views in real surveillance systems. Most approaches based on super-resolution (SR) excessively rely on the SR images, which may lead to the loss of low-resolution (LR) information. Meanwhile, the region-agnostic SR could pose interference to ReID. For this, we propose a jointly adaptive cross-resolution ReID framework that consists of a region-aware person super-resolution (RAPSR) and a resolution adaptive ReID (RAReID). RAPSR is equipped with spatial attention for enhancing crucial spatial regions in low-resolution (LR) images. RAReID extracts complementary features from LR and high-resolution (HR) images simultaneously and obtains more discriminative pedestrian representations through cascaded resolution adaptive feature fusion modules. Finally, by the joint training of RAPSR and RAReID, a greater ReID accuracy could be achieved. Extensive experiments demonstrate state-of-the-art performances on three derived and a native multi-resolution datasets. |
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| ISSN: | 2199-4536 2198-6053 |