We developed an end-to-end pipeline for brake light transition detection based on cognitive theories of anomaly-detection and model based systems engineering principles. Inspired by cognitive theory, we decompose the visual input stream into the submodalities color, shape, intensity and motion which is closely coupled with a graphical model. A memory module that contains priors is populated by exploitation of knowledge from specifications and simulation. High-fidelity 3D-Simulations have been created to populate motion memory, whereas low-fidelity 3D-projections have been used to create geometric priors. We captured real world sequences containing RGB images, GPS- and inertial measurement-data and annotated them with boundingboxes for cars and their tail and brakelight states, which are used to evaluate the proposed pipeline.


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    Titel :

    Anomaly detection for automotive visual signal transition estimation


    Beteiligte:
    Weis, Tobias (Autor:in) / Mundt, Martin (Autor:in) / Harding, Patrick (Autor:in) / Ramesh, Visvanathan (Autor:in)


    Erscheinungsdatum :

    01.10.2017


    Format / Umfang :

    487774 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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