Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

@article{Paisitkriangkrai2016PedestrianDW,
  title={Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning},
  author={Sakrapee Paisitkriangkrai and Chunhua Shen and Anton van den Hengel},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2016},
  volume={38},
  pages={1243-1257}
}
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 prescribed range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined… CONTINUE READING
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SVM pAUC: a new support vector method for optimizing partial AUC based on a tight convex upper bound

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