Video-based Detection Algorithms in FOLDOUT: Through-Foliage Detection in Ground-Based Border Surveillance

David Schreiber, Andreas Opitz

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung


The FOLDOUT project is concerned with through-foliage detection, which is an
unsolved important part of border surveillance. FOLDOUT builds a system that
combines various sensors and technologies to tackle this problem. This paper
reviews the work done by AIT in FOLDOUT concerning visual sensors (RGB and
thermal) for through-foliageobject detection. Through-foliagescenarios contain an
unprecedented amount of occlusion, specifically fragmented occlusion (e.g., looking
through the branches of a tree). It is demonstrated that current state-of-the-art
detectors based on deep learning approaches perform inadequately under
moderate to heavy fragmented occlusion. Variousstate-of-the-art and beyond stateof-the-art detection algorithms, based on deep learning as well as on other
approaches, dealt within FOLDOUT to detect objects in the case of fragmented
occlusion, are presented, discussed, and compared.
Seiten (von - bis)84-102
FachzeitschriftJournal of Defence & Security Technologies
PublikationsstatusVeröffentlicht - 2022

Research Field

  • Former Research Field - Surveillance and Protection


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