  We aim to study the modeling limitations of the commonly employed boosted
decision trees classifier. Inspired by the success of large, data-hungry visual
recognition models (e.g. deep convolutional neural networks), this paper
focuses on the relationship between modeling capacity of the weak learners,
dataset size, and dataset properties. A set of novel experiments on the Caltech
Pedestrian Detection benchmark results in the best known performance among
non-CNN techniques while operating at fast run-time speed. Furthermore, the
performance is on par with deep architectures (9.71% log-average miss rate),
while using only HOG+LUV channels as features. The conclusions from this study
are shown to generalize over different object detection domains as demonstrated
on the FDDB face detection benchmark (93.37% accuracy). Despite the impressive
performance, this study reveals the limited modeling capacity of the common
boosted trees model, motivating a need for architectural changes in order to
compete with multi-level and very deep architectures.
