Crowd density estimation, in general, is a challenging task due to the large variation of head sizes in the crowds. Existing methods always use a multi-column convolutional neural network (MCNN) to adapt to this variation, which results in an average effect in areas with different densities and brings a lot of noise to the density map. To address this problem, we propose a new method called the segmentation-aware prior network (SAPNet), which generates a high-quality density map without noise based on a coarse head-segmentation map. SAPNet is composed of two networks, i.e., a foreground-segmentation convolutional neural network (FS-CNN) as the front end and a crowd-regression convolutional neural network (CR-CNN) as the back end. With only the single dot annotation, we generate the ground truth of segmentation masks in heads. Then, based on the ground truth, FS-CNN outputs a coarse head-segmentation map, which helps eliminate the noise in regions without people in the density map. By inputting the head-segmentation map generated by the front end, CR-CNN performs accurate crowd counting estimation and generates a high-quality density map. We demonstrate SAPNet on four datasets (i.e., ShanghaiTech, UCF-CC-50, WorldExpo’10, and UCSD), and show the state-of-the-art performances on ShanghaiTech part B and UCF-CC-50 datasets.

通常，人群密度估计是一项艰巨的任务，因为人群中头部的大小差异很大。现有方法始终使用多列卷积神经网络（MCNN）来适应这种变化，从而在具有不同密度的区域中产生平均效果，并给密度图带来很多噪声。为了解决此问题，我们提出了一种称为分割感知先验网络（SAPNet）的新方法，该方法会基于粗略的头部分割图生成高质量的无噪声的密度图。SAPNet由两个网络组成，即前端分段卷积神经网络（FS-CNN）作为前端，而人群回归卷积神经网络（CR-CNN）作为后端。仅使用单点注释，就可以生成头部中的分割蒙版的地面真相。然后，基于地面事实，FS-CNN输出一个粗略的头部分割图，这有助于消除密度图中没有人的区域中的噪声。通过输入由前端生成的头部分割图，CR-CNN可以进行准确的人群计数估计并生成高质量的密度图。我们在四个数据集（即ShanghaiTech，UCF-CC-50，WorldExpo'10和UCSD）上演示了SAPNet，并展示了ShanghaiTech部分上的最新性能B和UCF-CC-50数据集。