Structured Inhomogeneous Density Map Learning for Crowd Counting
This work improves crowd counting accuracy for high-density scenes, which is incremental as it builds on existing density map methods.
The paper tackles the problem of crowd counting in extremely high-density scenes by addressing the inhomogeneous density distribution issue, achieving state-of-the-art performance on various challenging datasets.
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.