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Many typical applications of object detection operate within a prescribedfalse-positive range. In this situation the performance of a detector should beassessed on the basis of the area under the ROC curve over that range, ratherthan over the full curve, as the performance outside the range is irrelevant.  This measure is labelled as the partial area under the ROC curve (pAUC).Effective cascade-based classification, for example, depends on training nodeclassifiers that achieve the maximal detection rate at a moderate falsepositive rate, e.g., around 40% to 50%. We propose a novel ensemble learningmethod which achieves a maximal detection rate at a user-defined range of falsepositive rates by directly optimizing the partial AUC using structuredlearning. By optimizing for different ranges of false positive rates, theproposed method can be used to train either a single strong classifier or anode classifier forming part of a cascade classifier. Experimental results onboth synthetic and real-world data sets demonstrate the effectiveness of ourapproach, and we show that it is possible to train state-of-the-art pedestriandetectors using the proposed structured ensemble learning method.

Efficient pedestrian detection by directly optimize the partial area under the ROC curve