论文标题
代表区域的NMS：通过提案配对迈向拥挤的行人检测
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing
论文作者
论文摘要
尽管最近在行人探测中取得了重大进展，但拥挤的场景中的行人发现仍然具有挑战性。行人之间的重大阻塞对标准的非最大抑制（NMS）构成了巨大挑战。相对低的联盟交叉点阈值（IOU）导致缺少高度重叠的行人，而较高的人则带来了很多误报。为了避免这种困境，本文提出了一种新颖的代表性区域NMS方法，该方法利用了较少的可见部分，有效地消除了冗余盒子，而不会带来许多误报。为了获取可见的零件，提出了一种新型的配对模型（PBM），以同时预测行人的完整且可见的盒子。完整和可见的框构成了一对，作为模型的样品单位，因此确保了整个检测管道中两个盒子之间的较强对应关系。此外，在完整和可见的行人检测任务上，允许两个框的方便功能集成，以更好地性能。关于挑战的人类和城市代表基准的实验充分证明了拟议方法在拥挤的情况下对行人发现的有效性。
Although significant progress has been made in pedestrian detection recently, pedestrian detection in crowded scenes is still challenging. The heavy occlusion between pedestrians imposes great challenges to the standard Non-Maximum Suppression (NMS). A relative low threshold of intersection over union (IoU) leads to missing highly overlapped pedestrians, while a higher one brings in plenty of false positives. To avoid such a dilemma, this paper proposes a novel Representative Region NMS approach leveraging the less occluded visible parts, effectively removing the redundant boxes without bringing in many false positives. To acquire the visible parts, a novel Paired-Box Model (PBM) is proposed to simultaneously predict the full and visible boxes of a pedestrian. The full and visible boxes constitute a pair serving as the sample unit of the model, thus guaranteeing a strong correspondence between the two boxes throughout the detection pipeline. Moreover, convenient feature integration of the two boxes is allowed for the better performance on both full and visible pedestrian detection tasks. Experiments on the challenging CrowdHuman and CityPersons benchmarks sufficiently validate the effectiveness of the proposed approach on pedestrian detection in the crowded situation.
