  Pedestrian detection has been heavily studied in the last decade due to its
wide application. Despite incremental progress, crowd occlusion and hard
negatives are still challenging current state-of-the-art pedestrian detectors.
In this paper, we offer two approaches based on the general region-based
detection framework to tackle these challenges. Specifically, to address the
occlusion, we design a novel coulomb loss as a regulator on bounding box
regression, in which proposals are attracted by their target instance and
repelled by the adjacent non-target instances. For hard negatives, we propose
an efficient semantic-driven strategy for selecting anchor locations, which can
sample informative negative examples at training phase for classification
refinement. It is worth noting that these methods can also be applied to
general object detection domain, and trainable in an end-to-end manner. We
achieves consistently high performance on the Caltech-USA and CityPersons
benchmarks.
