NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios
Arturo Miguel Russell Bernal, Walter Scheirer, Jane Cleland-Huang

TL;DR
NOMAD is a comprehensive aerial dataset designed to evaluate human detection performance under occlusion in emergency response scenarios, featuring multi-scale, natural occlusions, and detailed annotations to improve computer vision models for search and rescue.
Contribution
The paper introduces NOMAD, a novel dataset specifically addressing occlusion challenges in aerial human detection for emergency response, with diverse scenarios and detailed visibility annotations.
Findings
Dataset includes 42,825 frames with occlusion annotations.
Provides a benchmark for evaluating detection models under occlusion.
Supports development of more robust aerial human detection algorithms.
Abstract
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…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Neural Network Applications · Robotics and Sensor-Based Localization
MethodsNone
