  Pedestrian detection in a crowd is a very challenging issue. This paper
addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to
better refine the bounding boxes given by detectors. The contributions are
threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression
threshold to an instance, according to the target density; (2) we design an
efficient subnetwork to learn density scores, which can be conveniently
embedded into both the single-stage and two-stage detectors; and (3) we achieve
state of the art results on the CityPersons and CrowdHuman benchmarks.
