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 this https URL .
Abstract (translated)
跨视角的人员重新识别（Person Re-Identification，简称ReID）在极远距离下从空中视角切换到地面视角时引入了一种独特的操作环境。在这种环境中，严重的分辨率退化、极端的角度变化、不稳定的动作线索以及服装差异共同削弱了现有基于外观假设的重识别系统的有效性。为了研究这一领域，我们提出了VReID-XFD，这是一个针对极远距离（XFD）空中到地面人员重新识别的视频基准和社区挑战。 VReID-XFD来源于DetReIDX数据集，并包含371个身份、11,288条轨迹片段以及超过1175万帧图像。这些图像在高度从5.8米至120米，视角角度从斜侧（30度）到正下方（90度），水平距离高达120米的情况下被捕捉下来。 该基准测试支持空中到空中、空中到地面以及地面到空中的评估，在严格的身份独立划分下进行，并提供了丰富的物理元数据。VReID-XFD-25挑战吸引了来自世界各地的10个团队，提交了数百份作品。系统分析揭示出随着高度和距离增加性能下降的趋势，正下方视角在所有场景下的普遍劣势，以及峰值性能与鲁棒性之间的权衡关系。 即使是最优的方法SAS-PReID，在空中到地面设置中也仅达到了43.93%的平均精度（mAP）。该数据集、标注及官方评估协议可在[提供的链接]处获取。
URL
https://arxiv.org/abs/2601.01312
