  Detecting pedestrian has been arguably addressed as a special topic beyond
general object detection. Although recent deep learning object detectors such
as Fast/Faster R-CNN [1, 2] have shown excellent performance for general object
detection, they have limited success for detecting pedestrian, and previous
leading pedestrian detectors were in general hybrid methods combining
hand-crafted and deep convolutional features. In this paper, we investigate
issues involving Faster R-CNN [2] for pedestrian detection. We discover that
the Region Proposal Network (RPN) in Faster R-CNN indeed performs well as a
stand-alone pedestrian detector, but surprisingly, the downstream classifier
degrades the results. We argue that two reasons account for the unsatisfactory
accuracy: (i) insufficient resolution of feature maps for handling small
instances, and (ii) lack of any bootstrapping strategy for mining hard negative
examples. Driven by these observations, we propose a very simple but effective
baseline for pedestrian detection, using an RPN followed by boosted forests on
shared, high-resolution convolutional feature maps. We comprehensively evaluate
this method on several benchmarks (Caltech, INRIA, ETH, and KITTI), presenting
competitive accuracy and good speed. Code will be made publicly available.
