Dense crowd counting aims to predict thousands of human instances from an
image, by calculating integrals of a density map over image pixels. Existing
approaches mainly suffer from the extreme density variances. Such density
pattern shift poses challenges even for multi-scale model ensembling. In this
paper, we propose a simple yet effective approach to tackle this problem.
First, a patch-level density map is extracted by a density estimation model and
further grouped into several density levels which are determined over full
datasets. Second, each patch density map is automatically normalized by an
online center learning strategy with a multipolar center loss. Such a design
can significantly condense the density distribution into several clusters, and
enable that the density variance can be learned by a single model. Extensive
experiments demonstrate the superiority of the proposed method. Our work
outperforms the state-of-the-art by 4.2%, 14.3%, 27.1% and 20.1% in MAE, on
ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF datasets,
respectively.

本文提出了一种简单有效的方法，通过密度估计模型提取小块密度图，并根据完整数据集确定的几个密度级别进行分组，再通过在线中心学习策略来自动规范化每个小块密度图，从而将密度分布显着凝聚成几个簇，并使密度差异可以被单个模型学习，从而在 MAE 上优于现有方法。