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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    Title :

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


    Contributors:
    Kallenbach, I. (author) / Schweiger, R. (author) / Palm, G. (author) / Lohlein, O. (author)


    Publication date :

    2006-01-01


    Size :

    1886062 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    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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