Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
This addresses the precision-recall trade-off in object detection for crowded scenes, offering an incremental improvement over existing methods.
The paper tackles the problem of non-maximum suppression (GreedyNMS) failing in crowded scenes by proposing Pairwise-NMS, which uses a deep learning network to predict relationships between overlapping boxes, achieving consistent improvements in datasets like MOT15, TUD-Crossing, and PETS.
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.