An algorithm for the on-board vision vehicle detection problem using a cascade of boosted classifiers is presented. Three families of features are compared: the rectangular filters (Haar-like features), the histograms of oriented gradient (HoG), and their combination (a concatenation of the two preceding features). A comparative study of the results of the generative (HoG features), discriminative (Haar-like features) detectors, and of their fusion is presented. These results show that the fusion combines the advantages of the other two detectors: generative classifiers eliminate 'easily' negative examples in the early layers of the cascade, while in the later layers, the discriminative classifiers generate a fine decision boundary removing the negative examples near the vehicle model. The best algorithm achieves good performances on a test set containing some 500 vehicle images: the detection rate is about 94% and the false-alarm rate per image is 0.0003.


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    Title :

    A cascade of boosted generative and discriminative classifiers for vehicle detection


    Contributors:


    Publication date :

    2008


    Size :

    12 Seiten, 11 Bilder, 3 Tabellen, 43 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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