Detector

Algorithm

Pedestrian

Engineering

Transport engineering

Subnetwork

Metallurgy

Materials science

Refining (metallurgy)

Telecommunications

Computer science

Pedestrian detection

Minimum bounding box

Computer network

Physical chemistry

Image (mathematics)

Artificial intelligence

Chemistry

Bounding overwatch

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.

Adaptive NMS: Refining Pedestrian Detection in a Crowd