  Training a robust classifier and an accurate box regressor are difficult for
occluded pedestrian detection. Traditionally adopted Intersection over Union
(IoU) measurement does not consider the occluded region of the object and leads
to improper training samples. To address such issue, a modification called
visible IoU is proposed in this paper to explicitly incorporate the visible
ratio in selecting samples. Then a newly designed box sign predictor is placed
in parallel with box regressor to separately predict the moving direction of
training samples. It leads to higher localization accuracy by introducing sign
prediction loss during training and sign refining in testing. Following these
novelties, we obtain state-of-the-art performance on CityPersons benchmark for
occluded pedestrian detection.
