We describe a method for training object detectors using a generalization of the cascade architecture, which results in a detection rate and speed comparable to that of the best published detectors while allowing for easier training and a detector with fewer features. In addition, the method allows for quickly calibrating the detector for a target detection rate, false positive rate or speed. One important advantage of our method is that it enables systematic exploration of the ROC surface, which characterizes the trade-off between accuracy and speed for a given classifier.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Robust object detection via soft cascade


    Beteiligte:
    Bourdev, L. (Autor:in) / Brandt, J. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    513065 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Background Learnable Cascade for Zero-Shot Object Detection

    Zheng, Ye / Huang, Ruoran / Han, Chuanqi et al. | British Library Conference Proceedings | 2021


    Object Detection Using a Cascade of 3D Models

    Pong, Hon-Keat / Cham, Tat-Jen | Springer Verlag | 2006


    Object Detection Using a Cascade of 3D Models

    Pong, H.-K. / Cham, T.-J. | British Library Conference Proceedings | 2006


    Vehicle detection for autonomous parking using a Soft-Cascade AdaBoost classifier

    Broggi, Alberto / Cardarelli, Elena / Cattani, Stefano et al. | IEEE | 2014


    VEHICLE DETECTION FOR AUTONOMOUS PARKING USING A SOFT-CASCADE ADABOOST CLASSIFIER

    Broggi, A. / Cardarelli, E. / Cattani, S. et al. | British Library Conference Proceedings | 2014