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

David Schreiber, Andreas Opitz

    Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

    Abstract

    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.
    OriginalspracheEnglisch
    Aufsatznummer5
    Seiten (von - bis)84-102
    FachzeitschriftJournal of Defence & Security Technologies
    Issue5
    PublikationsstatusVeröffentlicht - 2022

    Research Field

    • Former Research Field - Surveillance and Protection

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