Hybrid Channel Based Pedestrian Detection

•We extend the successful RPN+BF framework to combine handcrafted features and CNN features for pedestrian detection.•RoI-pooling is used to extract features from handcrafted and CNN channels, and with larger output resolution for the former.•We explore several handcrafted channels such as HOG+LUV, Checkerboards, and RotatedFilters in our framework.•Experimental results on Caltech pedestrian dataset demonstrate the effectiveness of the proposed framework.•When using a more advanced RPN, our approach can be further improved and get competitive results on the benchmarks.