论文标题
Beta R-CNN：从另一个角度研究行人检测
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
论文作者
论文摘要
最近在行人探测中取得了重大进展，但是在封闭和拥挤的场景中取得高性能仍然具有挑战性。它可以主要归因于行人的广泛表示形式，即2D轴对准边界框，这仅描述了对象的大致位置和大小。边界框将物体模拟为边界内的统一分布，使行人由于噪音很大而在被遮挡和拥挤的场景中无法区分。为了消除问题，我们提出了一种基于2D Beta分布的新颖表示，名为Beta表示。它通过明确构建全身和可见框之间的关系来描绘行人，并通过为像素分配不同的概率值来强调视觉质量的中心。结果，Beta表示可以更好地在拥挤的场景中使用名为BETANMS的新型NMS策略来区分高度重叠的实例。更重要的是，为充分利用Beta代表，提出了一个具有betahead和betamask配备的新型管道Beta beta beta beta r-CNN，从而在闭塞和拥挤的场景中导致了很高的检测性能。
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.
