  Although significant progress has been made in pedestrian detection recently,
pedestrian detection in crowded scenes is still challenging. The heavy
occlusion between pedestrians imposes great challenges to the standard
Non-Maximum Suppression (NMS). A relative low threshold of intersection over
union (IoU) leads to missing highly overlapped pedestrians, while a higher one
brings in plenty of false positives. To avoid such a dilemma, this paper
proposes a novel Representative Region NMS approach leveraging the less
occluded visible parts, effectively removing the redundant boxes without
bringing in many false positives. To acquire the visible parts, a novel
Paired-Box Model (PBM) is proposed to simultaneously predict the full and
visible boxes of a pedestrian. The full and visible boxes constitute a pair
serving as the sample unit of the model, thus guaranteeing a strong
correspondence between the two boxes throughout the detection pipeline.
Moreover, convenient feature integration of the two boxes is allowed for the
better performance on both full and visible pedestrian detection tasks.
Experiments on the challenging CrowdHuman and CityPersons benchmarks
sufficiently validate the effectiveness of the proposed approach on pedestrian
detection in the crowded situation.
