  Many typical applications of object detection operate within a prescribed
false-positive range. In this situation the performance of a detector should be
assessed on the basis of the area under the ROC curve over that range, rather
than 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 node
classifiers that achieve the maximal detection rate at a moderate false
positive rate, e.g., around 40% to 50%. We propose a novel ensemble learning
method which achieves a maximal detection rate at a user-defined range of false
positive rates by directly optimizing the partial AUC using structured
learning. By optimizing for different ranges of false positive rates, the
proposed method can be used to train either a single strong classifier or a
node classifier forming part of a cascade classifier. Experimental results on
both synthetic and real-world data sets demonstrate the effectiveness of our
approach, and we show that it is possible to train state-of-the-art pedestrian
detectors using the proposed structured ensemble learning method.
