Even with the advent of more sophisticated, data-hungry methods, boosted
decision trees remain extraordinarily successful for fast rigid object
detection, achieving top accuracy on numerous datasets. While effective, most
boosted detectors use decision trees with orthogonal (single feature) splits,
and the topology of the resulting decision boundary may not be well matched to
the natural topology of the data. Given highly correlated data, decision trees
with oblique (multiple feature) splits can be effective. Use of oblique splits,
however, comes at considerable computational expense. Inspired by recent work
on discriminative decorrelation of HOG features, we instead propose an
efficient feature transform that removes correlations in local neighborhoods.
The result is an overcomplete but locally decorrelated representation ideally
suited for use with orthogonal decision trees. In fact, orthogonal trees with
our locally decorrelated features outperform oblique trees trained over the
original features at a fraction of the computational cost. The overall
improvement in accuracy is dramatic: on the Caltech Pedestrian Dataset, we
reduce false positives nearly tenfold over the previous state-of-the-art.

该研究提出了一种有效的特征转换方法，该方法可以在计算成本的大幅降低的情况下，优于原始特征进行施工的正交决策树，并可用于快速坚硬物体检测。在 Caltech 行人数据集上的结果显示，该方法有效减少了假阳性，显著提高了检测精度。