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). 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 output learning. In order to achieve a high object detection performance, we propose a new approach to extract low-level visual features based on spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. 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 with spatially pooled features. The result is the current best reported performance on the Caltech-USA pedestrian detection dataset.

提出了一种基于结构化学习的新型集成学习方法，通过直接优化局部ROC曲线面积来实现用户定义范围内的最大检测速率，在此基础上，提出了一种新的基于空间池化的低级视觉特征提取方法。实验结果表明，此方法在Caltech-USA行人检测数据集上具有当前最佳性能的检测器。

利用空间池化特征和结构化集成学习进行行人检测