This paper presents a fast Histogram of Oriented Gradients (HOG) based weak classifier that is extremely fast to compute and highly discriminative. This feature set has been developed in an effort to balance the required processing and memory bandwidth so as to eliminate bottlenecks during run time evaluation. The feature set is the next generation in a series of features based on a novel precomputed image for HOG based features. It contains features which are more balanced in terms of processing and memory requirements than its predecessors, has a larger and richer feature space, and is more discriminant on a per feature basis.


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    Titel :

    Boosting a heterogeneous pool of fast HOG features for pedestrian and sign detection


    Beteiligte:
    Overett, Gary (Autor:in) / Petersson, Lars (Autor:in) / Andersson, Lars (Autor:in) / Pettersson, Niklas (Autor:in)


    Erscheinungsdatum :

    2009-06-01


    Format / Umfang :

    1555559 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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