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

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.