This paper proposes a novel approach for crowd counting in low to high
density scenarios in static images. Current approaches cannot handle huge crowd
diversity well and thus perform poorly in extreme cases, where the crowd
density in different regions of an image is either too low or too high, leading
to crowd underestimation or overestimation. The proposed solution is based on
the observation that detecting and handling such extreme cases in a specialized
way leads to better crowd estimation. Additionally, existing methods find it
hard to differentiate between the actual crowd and the cluttered background
regions, resulting in further count overestimation. To address these issues, we
propose a simple yet effective modular approach, where an input image is first
subdivided into fixed-size patches and then fed to a four-way classification
module labeling each image patch as low, medium, high-dense or no-crowd. This
module also provides a count for each label, which is then analyzed via a
specifically devised novel decision module to decide whether the image belongs
to any of the two extreme cases (very low or very high density) or a normal
case. Images, specified as high- or low-density extreme or a normal case, pass
through dedicated zooming or normal patch-making blocks respectively before
routing to the regressor in the form of fixed-size patches for crowd estimate.
Extensive experimental evaluations demonstrate that the proposed approach
outperforms the state-of-the-art methods on four benchmarks under most of the
evaluation criteria.

本文提出了一种新的方法，在静态图像中高效地进行低到高密度场景下的人群计数，该方法通过模块化的形式对极端情况进行特别分析，有效地解决了现有方法不能满足高度多样化人群分类和背景混杂的问题，并经过了广泛的实验评估，证明在不同标准下优于现有技术。