  Non-maximum suppression (NMS) is used in virtually all state-of-the-art
object detection pipelines. While essential object detection ingredients such
as features, classifiers, and proposal methods have been extensively researched
surprisingly little work has aimed to systematically address NMS. The de-facto
standard for NMS is based on greedy clustering with a fixed distance threshold,
which forces to trade-off recall versus precision. We propose a convnet
designed to perform NMS of a given set of detections. We report experiments on
a synthetic setup, and results on crowded pedestrian detection scenes. Our
approach overcomes the intrinsic limitations of greedy NMS, obtaining better
recall and precision.
