Search and Rescue with Airborne Optical Sectioning

TL;DR

This paper demonstrates that Airborne Optical Sectioning (AOS) enhances automated person detection under occlusion by integrating multi-perspective drone images, achieving high precision and recall, and enabling effective search and rescue in dense environments.

Contribution

The study introduces the use of AOS for improved person detection in occluded environments, providing a novel imaging technique for search and rescue applications.

Findings

Achieved 96% precision and 93% recall in person detection.

Enabled detection of lost or injured individuals in dense forests.

Laid groundwork for future autonomous rescue technologies.

Abstract

We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification. Here, we employed image integration by Airborne Optical Sectioning (AOS)---a synthetic aperture imaging technique that uses camera drones to capture unstructured thermal light fields---to achieve this with a precision/recall of 96/93%. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with use of AOS integral images. Our findings lay the foundation for effective future search and rescue technologies that can be applied in combination with autonomous or manned aircraft. They can also be beneficial for other fields that currently suffer from inaccurate classification of partially occluded people, animals, or objects.