  As the post-processing step for object detection, non-maximum suppression
(GreedyNMS) is widely used in most of the detectors for many years. It is
efficient and accurate for sparse scenes, but suffers an inevitable trade-off
between precision and recall in crowded scenes. To overcome this drawback, we
propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship
network that is based on deep learning is learned to predict if two overlapping
proposal boxes contain two objects or zero/one object, which can handle
multiple overlapping objects effectively. Through neatly coupling with
GreedyNMS without losing efficiency, consistent improvements have been achieved
in heavily occluded datasets including MOT15, TUD-Crossing and PETS. In
addition, Pairwise-NMS can be integrated into any learning based detectors
(Both of Faster-RCNN and DPM detectors are tested in this paper), thus building
a bridge between GreedyNMS and end-to-end learning detectors.
