%%% -*-BibTeX-*-
@inproceedings{ICCV13Pai,
    author      = {Sakrapee Paisitkriangkrai and Chunhua Shen and Anton {van den Hengel}},
    title       = {Efficient pedestrian detection by directly optimizing the partial area under the {ROC} curve},
    year        = {2013},
    month       = {December},
    booktitle   = {IEEE International  Conference on Computer Vision (ICCV'13)},
    address     = {Sydney, Australia},
    venue       = {ICCV},
    pages       = {},
    volume      = {},
    publisher   = {},
    note        = {},
    eprint      = {1310.0900},
    url         = {},
    pdf         = {http://hdl.handle.net/2440/83158},
    project     = {},
    abstract    = {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.},
}