This study describes an effective method for detecting, tracking and classifying vehicles during night-time in order to support automotive adaptive illumination applications. The hereby described software framework, which computes the relative position, velocity and estimated class of all detected vehicles, integrates multiple processing stages. Firstly, an image segmentation using a threshold method to detect all light sources in the image. Secondly, possible pairs of head- and taillight are clustered using geometrical information. Thirdly, all detected ob-jects are tracked using a Kalman-Filter to increase resolution and robustness of the algorithm. Lastly, a method for computing distance and velocity for all classi-fied objects (e.g. cars, trucks, bikes) is presented. The system is tested to run in realtime and some results and conclusions are offered at the end


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vehicle detection, classification and position estimation based on monocular video data during night-time


    Beteiligte:
    Firl, Jonas (Autor:in) / Hoerter, Marko H. (Autor:in) / Lauer, Martin (Autor:in) / Stiller, Christoph (Autor:in)


    Erscheinungsdatum :

    2009


    Format / Umfang :

    13 Seiten, 12 Bilder, 10 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Distance estimation and vehicle position detection based on monocular camera

    Ali, Abduladhem Abdulkareem / Hussein, Hussein Alaa | IEEE | 2016


    Real Time Speed Estimation From Monocular Video

    Temiz, M. S. / Kulur, S. / Dogan, S. | BASE | 2012

    Freier Zugriff

    REAL TIME MONOCULAR VEHICLE VELOCITY ESTIMATION USING SYNTHETIC DATA

    McCraith, Robert / Neumann, Lukas / Vedaldi, Andrea | British Library Conference Proceedings | 2021


    Real Time Monocular Vehicle Velocity Estimation using Synthetic Data

    McCraith, Robert / Neumann, Lukas / Vedaldi, Andrea | IEEE | 2021