A necessary condition to perform a fully autonomous driving system in urban environment is to detect object types in real scenes. Visual object recognition is a key solution, but multi-object detection still remain unsolved. In this paper, we present a fast and efficient multi-object detection system built to recognize, at the same time, pedestrians cars and bicycles. For each target type, we construct a holistic detector in a cascade manner, using a dense overlapping grid based on histograms of oriented gradients (HOG). The selection of HOG features is obtained through a learning process using AdaBoost algorithm. Experiments have been conducted on the car-like robot Robucar, where the single detectors are combined and implemented on its embedded computer, which is endowed with a modular software platform. Results are promising as the system can process up to 20 fps with VGA images.


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    Titel :

    HOG based multi-object detection for urban navigation


    Beteiligte:
    Chayeb, A. (Autor:in) / Ouadah, N. (Autor:in) / Tobal, Z. (Autor:in) / Lakrouf, M. (Autor:in) / Azouaoui, O. (Autor:in)


    Erscheinungsdatum :

    2014-10-01


    Format / Umfang :

    2219033 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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