  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.

Computer Vision and Pattern Recognition