  Pedestrian detection has achieved great improvements with the help of
Convolutional Neural Networks (CNNs). CNN can learn high-level features from
input images, but the insufficient spatial resolution of CNN feature channels
(feature maps) may cause a loss of information, which is harmful especially to
small instances. In this paper, we propose a new pedestrian detection
framework, which extends the successful RPN+BF framework to combine handcrafted
features and CNN features. RoI-pooling is used to extract features from both
handcrafted channels (e.g. HOG+LUV, CheckerBoards or RotatedFilters) and CNN
channels. Since handcrafted channels always have higher spatial resolution than
CNN channels, we apply RoI-pooling with larger output resolution to handcrafted
channels to keep more detailed information. Our ablation experiments show that
the developed handcrafted features can reach better detection accuracy than the
CNN features extracted from the VGG-16 net, and a performance gain can be
achieved by combining them. Experimental results on Caltech pedestrian dataset
with the original annotations and the improved annotations demonstrate the
effectiveness of the proposed approach. When using a more advanced RPN in our
framework, our approach can be further improved and get competitive results on
both benchmarks.
