  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.
