Search and rescue with airborne optical sectioning

David C. Schedl, Indrajit Kurmi, Oliver Bimber

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


In the future, rescuing lost, ill or injured persons will increasingly be carried out by autonomous drones. However, discovering humans in densely forested terrain is challenging because of occlusion, and robust detection mechanisms are required. We show that automated person detection under occlusion conditions can be notably improved by combining multi-perspective images before classification. Here, we employ 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 and recall of 96% and 93%, respectively. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with the 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.

Original languageEnglish
Pages (from-to)783-790
Number of pages8
JournalNature Machine Intelligence
Issue number12
Publication statusPublished - 23 Dec 2020
Externally publishedYes


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