Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Grundlagen des Maschinellen Lernens
    ( 2021)
    Bauckhage, Christian
    ;
    Hübner, Wolfgang
    ;
    Hug, Ronny
    ;
    Paaß, Gerhard
    ;
    Rüping, Stefan
    Zu definieren, was die menschliche Intelligenz sowie intelligentes Handeln – und da­mit auch die Künstliche Intelligenz – ausmacht, ist außerordentlich schwer und be­schäftigt Philosophen und Psychologen seit Jahrtausenden. Allgemein anerkannt istaber, dass die Fähigkeit zu lernen ein zentrales Merkmal vonIntelligenzist. So ist auchdas Forschungsgebiet desMaschinellen Lernens(engl.machine learning, ML) ein zen­traler Teil der Künstlichen Intelligenz, das hinter vielen aktuellen Erfolgen von KI-Sys­temen steckt.
  • Publication
    State estimation for tracking in image space with a de- and re-coupled IMM filter
    ( 2018)
    Becker, Stefan
    ;
    Hübner, Wolfgang
    ;
    Arens, Michael
    Estimating the motion state of objects is a central component of most visual tracking pipelines. Visual tracking relies on observations in scale space generated by an appearance model. Under real-life conditions, it is obvious to assume that the dynamic of a tracked object changes over time. A popular solution for considering such varying system characteristics is the Interacting Multiple Model (IMM) filter. Usually, the motion of objects is modeled using position, velocity, and acceleration. Although it seems obvious that different image space dimensions can be combined in one overall system state, this naïve approach may fail under various circumstances. Toward this end, we demonstrate the benefit of decoupling the state estimate of an IMM filter in case of relying solely on the output of a visual tracker. Further, a state re-coupling scheme is introduced which helps to better deal with the corresponding measurement uncertainties of such a tracking pipeline. The proposed decoupled and re-coupled IMM filters are evaluated on publicly available datasets.