We propose a simple yet effective approach to the problem of pedestrian
detection which outperforms the current state-of-the-art. Our new features are
built on the basis of low-level visual features and spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. We then directly optimise the partial
area under the ROC curve (\pAUC) measure, which concentrates detection
performance in the range of most practical importance. The combination of these
factors leads to a pedestrian detector which outperforms all competitors on all
of the standard benchmark datasets. We advance state-of-the-art results by
lowering the average miss rate from $13\%$ to $11\%$ on the INRIA benchmark,
$41\%$ to $37\%$ on the ETH benchmark, $51\%$ to $42\%$ on the TUD-Brussels
benchmark and $36\%$ to $29\%$ on the Caltech-USA benchmark.

本研究提出一种简单而有效的行人检测方法，其基于低级视觉特征和空间池化，将部分 ROC 曲线下面积直接优化，表现出色优于当前所有竞争方法，平均错误率最低达到了 11％，与多个基准数据集相比，取得了最先进的结果。

使用空间池化特征加强行人检测效果

Strengthening the Effectiveness of Pedestrian Detection with Spatially  Pooled Features

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.

提出了一种使用结构化学习直接优化 ROC 曲线下假阳性率范围内的部分面积的新型集成学习方法，该方法有效地训练出最先进的行人检测器。