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.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A cascade of boosted generative and discriminative classifiers for vehicle detection


    Beteiligte:
    Negri, Pablo (Autor:in) / Clady, Xavier (Autor:in) / Shehzad Muhammad Haniv (Autor:in) / Prevost, Lionel (Autor:in)


    Erscheinungsdatum :

    2008


    Format / Umfang :

    12 Seiten, 11 Bilder, 3 Tabellen, 43 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    People Detection in Complex Scene Using a Cascade of Boosted Classifiers Based on Haarlike-features

    Siala, M. / Khlifa, N. / Bremond, F. et al. | British Library Conference Proceedings | 2009


    Co-trained generative and discriminative trackers with cascade particle filter

    Dinh, T. B. / Yu, Q. / Medioni, G. r. | British Library Online Contents | 2014


    Boosted translation-tolerable classifiers for fast object detection

    Zheng, W. / Liang, L. / Chang, H. et al. | British Library Online Contents | 2012