Crowd counting is a highly challenging problem in computer vision and machine learning. Most previous methods have focused on consistent density crowds, i.e., either a sparse or a dense crowd, meaning they performed well in global estimation while neglecting local accuracy. To make crowd counting more useful in the real world, we propose a new perspective, named pan-density crowd counting, which aims to count people in varying density crowds. Specifically, we propose the Pan-Density Network (PaDNet) which is composed of the following critical components. First, the Density-Aware Network (DAN) contains multiple subnetworks pretrained on scenarios with different densities. This module is capable of capturing pandensity information. Second, the Feature Enhancement Layer (FEL) effectively captures the global and local contextual features and generates a weight for each density-specific feature. Third, the Feature Fusion Network (FFN) embeds spatial context and fuses these density-specific features. Further, the metrics Patch MAE (PMAE) and Patch RMSE (PRMSE) are proposed to better evaluate the performance on the global and local estimations. Extensive experiments on four crowd counting benchmark datasets, the ShanghaiTech, the UCF-CC-50, the UCSD, and the UCFQNRF, indicate that PaDNet achieves state-of-the-art recognition performance and high robustness in pan-density crowd counting.

中文翻译：

---

PaDNet：泛密度人群计数。


人群计数是计算机视觉和机器学习中一个极具挑战性的问题。以前的大多数方法都关注一致密度的人群，即稀疏或密集的人群，这意味着它们在全局估计中表现良好，而忽略了局部精度。为了使人群计数在现实世界中更加有用，我们提出了一种新的视角，称为泛密度人群计数，旨在对不同密度人群中的人数进行计数。具体来说，我们提出了泛密度网络（PaDNet），它由以下关键组件组成。首先，密度感知网络（DAN）包含针对不同密度场景进行预训练的多个子网络。该模块能够捕获全密度信息。其次，特征增强层（FEL）有效地捕获全局和局部上下文特征，并为每个特定于密度的特征生成权重。第三，特征融合网络（FFN）嵌入空间上下文并融合这些特定于密度的特征。此外，提出了 Patch MAE (PMAE) 和 Patch RMSE (PRMSE) 度量来更好地评估全局和局部估计的性能。在上海科技大学、UCF-CC-50、UCSD 和 UCFQNRF 四个人群计数基准数据集上进行的广泛实验表明，PaDNet 在全密度人群计数中实现了最先进的识别性能和高鲁棒性。