VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912801604239360 |
|---|---|
| author | Hambarde, Kailash A. Proença, Hugo Rashidunnabi, Md Samale, Pranita Yang, Qiwei Zhang, Pingping Gong, Zijing Wang, Yuhao Zhang, Xi Qu, Ruoshui He, Qiaoyun Zhang, Yuhang Nguyen, Thi Ngoc Ha Mai, Tien-Dung Kang, Cheng-Jun Lin, Yu-Fan Jiang, Jin-Hui Hsu, Chih-Chung Endrei, Tamás Cserey, György Rajbhandari, Ashwat |
| author_facet | Hambarde, Kailash A. Proença, Hugo Rashidunnabi, Md Samale, Pranita Yang, Qiwei Zhang, Pingping Gong, Zijing Wang, Yuhao Zhang, Xi Qu, Ruoshui He, Qiaoyun Zhang, Yuhang Nguyen, Thi Ngoc Ha Mai, Tien-Dung Kang, Cheng-Jun Lin, Yu-Fan Jiang, Jin-Hui Hsu, Chih-Chung Endrei, Tamás Cserey, György Rajbhandari, Ashwat |
| contents | Person re-identification (ReID) across aerial and ground views at extreme far distances introduces a distinct operating regime where severe resolution degradation, extreme viewpoint changes, unstable motion cues, and clothing variation jointly undermine the appearance-based assumptions of existing ReID systems. To study this regime, we introduce VReID-XFD, a video-based benchmark and community challenge for extreme far-distance (XFD) aerial-to-ground person re-identification. VReID-XFD is derived from the DetReIDX dataset and comprises 371 identities, 11,288 tracklets, and 11.75 million frames, captured across altitudes from 5.8 m to 120 m, viewing angles from oblique (30 degrees) to nadir (90 degrees), and horizontal distances up to 120 m. The benchmark supports aerial-to-aerial, aerial-to-ground, and ground-to-aerial evaluation under strict identity-disjoint splits, with rich physical metadata. The VReID-XFD-25 Challenge attracted 10 teams with hundreds of submissions. Systematic analysis reveals monotonic performance degradation with altitude and distance, a universal disadvantage of nadir views, and a trade-off between peak performance and robustness. Even the best-performing SAS-PReID method achieves only 43.93 percent mAP in the aerial-to-ground setting. The dataset, annotations, and official evaluation protocols are publicly available at https://www.it.ubi.pt/DetReIDX/ . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01312 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results Hambarde, Kailash A. Proença, Hugo Rashidunnabi, Md Samale, Pranita Yang, Qiwei Zhang, Pingping Gong, Zijing Wang, Yuhao Zhang, Xi Qu, Ruoshui He, Qiaoyun Zhang, Yuhang Nguyen, Thi Ngoc Ha Mai, Tien-Dung Kang, Cheng-Jun Lin, Yu-Fan Jiang, Jin-Hui Hsu, Chih-Chung Endrei, Tamás Cserey, György Rajbhandari, Ashwat Computer Vision and Pattern Recognition Person re-identification (ReID) across aerial and ground views at extreme far distances introduces a distinct operating regime where severe resolution degradation, extreme viewpoint changes, unstable motion cues, and clothing variation jointly undermine the appearance-based assumptions of existing ReID systems. To study this regime, we introduce VReID-XFD, a video-based benchmark and community challenge for extreme far-distance (XFD) aerial-to-ground person re-identification. VReID-XFD is derived from the DetReIDX dataset and comprises 371 identities, 11,288 tracklets, and 11.75 million frames, captured across altitudes from 5.8 m to 120 m, viewing angles from oblique (30 degrees) to nadir (90 degrees), and horizontal distances up to 120 m. The benchmark supports aerial-to-aerial, aerial-to-ground, and ground-to-aerial evaluation under strict identity-disjoint splits, with rich physical metadata. The VReID-XFD-25 Challenge attracted 10 teams with hundreds of submissions. Systematic analysis reveals monotonic performance degradation with altitude and distance, a universal disadvantage of nadir views, and a trade-off between peak performance and robustness. Even the best-performing SAS-PReID method achieves only 43.93 percent mAP in the aerial-to-ground setting. The dataset, annotations, and official evaluation protocols are publicly available at https://www.it.ubi.pt/DetReIDX/ . |
| title | VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.01312 |