  Person re-ID matches persons across multiple non-overlapping cameras. Despite
the increasing deployment of airborne platforms in surveillance, current
existing person re-ID benchmarks' focus is on ground-ground matching and very
limited efforts on aerial-aerial matching. We propose a new benchmark dataset -
AG-ReID, which performs person re-ID matching in a new setting: across aerial
and ground cameras. Our dataset contains 21,983 images of 388 identities and 15
soft attributes for each identity. The data was collected by a UAV flying at
altitudes between 15 to 45 meters and a ground-based CCTV camera on a
university campus. Our dataset presents a novel elevated-viewpoint challenge
for person re-ID due to the significant difference in person appearance across
these cameras. We propose an explainable algorithm to guide the person re-ID
model's training with soft attributes to address this challenge. Experiments
demonstrate the efficacy of our method on the aerial-ground person re-ID task.
The dataset will be published and the baseline codes will be open-sourced at
https://github.com/huynguyen792/AG-ReID to facilitate research in this area.
