  As unmanned aerial vehicles (UAVs) become more accessible with a growing
range of applications, the potential risk of UAV disruption increases. Recent
development in deep learning allows vision-based counter-UAV systems to detect
and track UAVs with a single camera. However, the coverage of a single camera
is limited, necessitating the need for multicamera configurations to match UAVs
across cameras - a problem known as re-identification (reID). While there has
been extensive research on person and vehicle reID to match objects across time
and viewpoints, to the best of our knowledge, there has been no research in UAV
reID. UAVs are challenging to re-identify: they are much smaller than
pedestrians and vehicles and they are often detected in the air so appear at a
greater range of angles. Because no UAV data sets currently use multiple
cameras, we propose the first new UAV re-identification data set, UAV-reID,
that facilitates the development of machine learning solutions in this emerging
area. UAV-reID has two settings: Temporally-Near to evaluate performance across
views to assist tracking frameworks, and Big-to-Small to evaluate reID
performance across scale and to allow early reID when UAVs are detected from a
long distance. We conduct a benchmark study by extensively evaluating different
reID backbones and loss functions. We demonstrate that with the right setup,
deep networks are powerful enough to learn good representations for UAVs,
achieving 81.9% mAP on the Temporally-Near setting and 46.5% on the challenging
Big-to-Small setting. Furthermore, we find that vision transformers are the
most robust to extreme variance of scale.
