NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency
  Response Scenarios
Arturo Miguel Russell Bernal Walter Scheirer Jane Cleland-Huang
With the increasing reliance on small Unmanned Aerial Systems (sUAS) for
Emergency Response Scenarios, such as Search and Rescue, the integration of
computer vision capabilities has become a key factor in mission success.
Nevertheless, computer vision performance for detecting humans severely
degrades when shifting from ground to aerial views. Several aerial datasets
have been created to mitigate this problem, however, none of them has
specifically addressed the issue of occlusion, a critical component in
Emergency Response Scenarios. Natural Occluded Multi-scale Aerial Dataset
(NOMAD) presents a benchmark for human detection under occluded aerial views,
with five different aerial distances and rich imagery variance. NOMAD is
composed of 100 different Actors, all performing sequences of walking, laying
and hiding. It includes 42,825 frames, extracted from 5.4k resolution videos,
and manually annotated with a bounding box and a label describing 10 different
visibility levels, categorized according to the percentage of the human body
visible inside the bounding box. This allows computer vision models to be
evaluated on their detection performance across different ranges of occlusion.
NOMAD is designed to improve the effectiveness of aerial search and rescue and
to enhance collaboration between sUAS and humans, by providing a new benchmark
dataset for human detection under occluded aerial views.