{"ID":2822765,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.01312","arxiv_id":"2601.01312","title":"VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results","abstract":"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/ .","short_abstract":"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 stud...","url_abs":"https://arxiv.org/abs/2601.01312","url_pdf":"https://arxiv.org/pdf/2601.01312v1","authors":"[\"Kailash A. Hambarde\",\"Hugo Proença\",\"Md Rashidunnabi\",\"Pranita Samale\",\"Qiwei Yang\",\"Pingping Zhang\",\"Zijing Gong\",\"Yuhao Wang\",\"Xi Zhang\",\"Ruoshui Qu\",\"Qiaoyun He\",\"Yuhang Zhang\",\"Thi Ngoc Ha Nguyen\",\"Tien-Dung Mai\",\"Cheng-Jun Kang\",\"Yu-Fan Lin\",\"Jin-Hui Jiang\",\"Chih-Chung Hsu\",\"Tamás Endrei\",\"György Cserey\",\"Ashwat Rajbhandari\"]","published":"2026-01-04T00:27:16Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","project_urls":"[\"https://www.it.ubi.pt/DetReIDX/\"]","has_code":false,"code_links":[{"ID":605444,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2822765,"paper_url":"https://arxiv.org/abs/2601.01312","paper_title":"VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results","repo_url":"https://github.com/kailashhambarde/DetReIDX","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}