VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results

TL;DR

This paper introduces VReID-XFD, a challenging new benchmark for video-based person re-identification at extreme far distances, highlighting the difficulties and current performance limits in aerial-to-ground scenarios.

Contribution

The paper presents VReID-XFD, a large-scale, diverse dataset and benchmark for extreme far-distance person re-identification, along with analysis of current methods' performance and challenges.

Findings

Performance decreases with altitude and distance

Nadir views are universally more challenging

Best methods achieve only 43.93% mAP in aerial-to-ground

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/ .