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 |
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Springer
2025-04-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01881-1 |
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| _version_ | 1849733919365136384 |
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| author | Caihong Yuan Zhijie Guan Yuanchen Xu Xiaopan Chen Xiaoke Zhu Wenjuan Liang |
| author_facet | Caihong Yuan Zhijie Guan Yuanchen Xu Xiaopan Chen Xiaoke Zhu Wenjuan Liang |
| author_sort | Caihong Yuan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-df3bffbfd4d3411aaf2bf2ad2bc0a019 |
| institution | DOAJ |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-df3bffbfd4d3411aaf2bf2ad2bc0a0192025-08-20T03:07:55ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-04-0111611110.1007/s40747-025-01881-1Jointly adaptive cross-resolution person re-identification on super-resolutionCaihong Yuan0Zhijie Guan1Yuanchen Xu2Xiaopan Chen3Xiaoke Zhu4Wenjuan Liang5Henan Province Spatial Information Processing Engineering Technology Research Center Henan Engineering Research Center of Intelligent Technology and Application, Henan UniversityHenan Province Spatial Information Processing Engineering Technology Research Center Henan Engineering Research Center of Intelligent Technology and Application, Henan UniversityHenan Province Spatial Information Processing Engineering Technology Research Center Henan Engineering Research Center of Intelligent Technology and Application, Henan UniversityHenan Province Spatial Information Processing Engineering Technology Research Center Henan Engineering Research Center of Intelligent Technology and Application, Henan UniversityHenan Province Spatial Information Processing Engineering Technology Research Center Henan Engineering Research Center of Intelligent Technology and Application, Henan UniversityHenan Province Spatial Information Processing Engineering Technology Research Center Henan Engineering Research Center of Intelligent Technology and Application, Henan UniversityAbstract 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.https://doi.org/10.1007/s40747-025-01881-1Cross-resolutionPerson re-identificationRegion-aware super-resolutionResolution adaptive |
| spellingShingle | Caihong Yuan Zhijie Guan Yuanchen Xu Xiaopan Chen Xiaoke Zhu Wenjuan Liang Jointly adaptive cross-resolution person re-identification on super-resolution Complex & Intelligent Systems Cross-resolution Person re-identification Region-aware super-resolution Resolution adaptive |
| title | Jointly adaptive cross-resolution person re-identification on super-resolution |
| title_full | Jointly adaptive cross-resolution person re-identification on super-resolution |
| title_fullStr | Jointly adaptive cross-resolution person re-identification on super-resolution |
| title_full_unstemmed | Jointly adaptive cross-resolution person re-identification on super-resolution |
| title_short | Jointly adaptive cross-resolution person re-identification on super-resolution |
| title_sort | jointly adaptive cross resolution person re identification on super resolution |
| topic | Cross-resolution Person re-identification Region-aware super-resolution Resolution adaptive |
| url | https://doi.org/10.1007/s40747-025-01881-1 |
| work_keys_str_mv | AT caihongyuan jointlyadaptivecrossresolutionpersonreidentificationonsuperresolution AT zhijieguan jointlyadaptivecrossresolutionpersonreidentificationonsuperresolution AT yuanchenxu jointlyadaptivecrossresolutionpersonreidentificationonsuperresolution AT xiaopanchen jointlyadaptivecrossresolutionpersonreidentificationonsuperresolution AT xiaokezhu jointlyadaptivecrossresolutionpersonreidentificationonsuperresolution AT wenjuanliang jointlyadaptivecrossresolutionpersonreidentificationonsuperresolution |