In this paper, we aim at tackling the problem of crowd counting in extremely high-density scenes, which contain hundreds, or even thousands of people. We begin by a comprehensive analysis of the most widely used density map-based methods, and demonstrate how easily existing methods are affected by the inhomogeneous density distribution problem, e.g., causing them to be sensitive to outliers, or be hard to optimized. We then present an extremely simple solution to the inhomogeneous density distribution problem, which can be intuitively summarized as extending the density map from 2D to 3D, with the extra dimension implicitly indicating the density level. Such solution can be implemented by a single Density-Aware Network, which is not only easy to train, but also can achieve the state-of-art performance on various challenging datasets.

本文旨在处理极高密度场景下的人群计数问题，通过综合分析使用最广泛的基于密度图的方法并展示现有方法如何容易受到密度分布不均匀问题影响，证明其对偏离值敏感或难以优化的影响。接着，我们提出了一个极其简单的解决方案，即将密度图从二维扩展到三维，用额外的维度隐含地表示密度级别解决不均匀密度分布问题。这种解决方案可以通过单个密度感知网络实现，不仅易于训练，而且可以在各种具有挑战性的数据集上实现最先进的表现。

面向人群计数的结构化非均匀密度图学习