Recently significant progress has been made in pedestrian detection, but it remains challenging to achieve high performance in occluded and crowded scenes. It could be attributed mostly to the widely used representation of pedestrians, i.e., 2D axis-aligned bounding box, which just describes the approximate location and size of the object. Bounding box models the object as a uniform distribution within the boundary, making pedestrians indistinguishable in occluded and crowded scenes due to much noise. To eliminate the problem, we propose a novel representation based on 2D beta distribution, named Beta Representation. It pictures a pedestrian by explicitly constructing the relationship between full-body and visible boxes, and emphasizes the center of visual mass by assigning different probability values to pixels. As a result, Beta Representation is much better for distinguishing highly-overlapped instances in crowded scenes with a new NMS strategy named BetaNMS. What's more, to fully exploit Beta Representation, a novel pipeline Beta R-CNN equipped with BetaHead and BetaMask is proposed, leading to high detection performance in occluded and crowded scenes.

该研究提出了一种基于2D beta分布的新颖表示方法，即Beta Representation，它通过显式构建全身和可见框之间的关系，并通过给像素分配不同的概率值来强调中心可视质心，从而在拥挤场景中区分高度重叠的实例，进而提高行人检测的性能。此外，研究还提出了一种新的基于Beta Representation的NMS策略以及 Beta R-CNN 系统，使其在遮挡和拥挤场景中的行人检测中表现出色。

Beta R-CNN：从另一角度审视行人检测