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.


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    Title :

    Robust object detection via soft cascade


    Contributors:
    Bourdev, L. (author) / Brandt, J. (author)


    Publication date :

    2005-01-01


    Size :

    513065 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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