This paper presents the results of an all-day-long pedestrian classification system based on an AdaBoost cascade meta-algorithm. The underlying idea is to use a Haar-features-based AdaBoost together with an ad-hoc-features-based AdaBoost system in order to reach a better pedestrian classification. A specific night-time pedestrian classification is developed in order to obtain a system that can be used also in poorly illuminated environments. These classifiers are joined together using a cascade AdaBoost system that uses the output of the previous classifiers to obtain a final classification for the area. In the paper the night time and the ad-hoc features systems are presented together with the cascade classification and quantitative results.


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    Titel :

    Day and night pedestrian detection using cascade AdaBoost system


    Beteiligte:
    Cerri, Pietro (Autor:in) / Gatti, Luca (Autor:in) / Mazzei, Luca (Autor:in) / Pigoni, Fabio (Autor:in) / Ho Gi Jung, (Autor:in)


    Erscheinungsdatum :

    01.09.2010


    Format / Umfang :

    1138289 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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