Boosted cascades for fast and reliable object detection for one object class were introduced by Viola et al. [8]. Using this scheme for multi-class detection requires parallel usage of multiple cascades and increases computation time. We present an extension to the cascade which examines multiple classes jointly in the first stages of the cascade. Adaboost is selecting common features for all considered object classes, which are then computed only once and thus reduce the computation time of the overall system. We also show how to define the search-window, as it needs to be adjusted to the specific objects. The multi-class capable cascade is applied to traffic scenes on rural roads where pedestrians and reflection posts are detected.


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    Titel :

    Multi-class Object Detection in Vision Systems Using a Hierarchy of Cascaded Classifiers


    Beteiligte:
    Kallenbach, I. (Autor:in) / Schweiger, R. (Autor:in) / Palm, G. (Autor:in) / Lohlein, O. (Autor:in)


    Erscheinungsdatum :

    2006-01-01


    Format / Umfang :

    1886062 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-class Object Detection in Vision Systems Using a Hierarchy of Cascaded Classifiers

    Kallenbach, I. / Schweiger, R. / Palm, G. et al. | British Library Conference Proceedings | 2006




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    Heisele, B. / Serre, T. / Mukherjee, S. et al. | IEEE | 2001


    Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images

    Heisele, B. / Serre, T. / Mukherjee, S. et al. | British Library Conference Proceedings | 2001