Crowd counting is an important problem in computer vision, whose application can be found in a wide range of tasks. Although this problem has been well studied, how to effectively deal with scale variations and perspective distortions is still a challenge. High-quality crowd density map depends heavily on how well these problems are solved. In this paper, we propose a novel network architecture called Attentive Multi-stage CNN for Crowd Counting (AMCNN). The AMCNN contains two subnetworks, i.e., hierarchical density estimator(HDE) and auxiliary count classifier (AUCC). The HDE adopts a hierarchical strategy to mine semantic features in a coarse-to-fine manner to tackle the problem of scale changes and perspective distortions. And the obtained composite features are used to generate the final density map. In addition, to further improve the density map quality, a soft attention mechanism is integrated into the AMCNN to distinct the foreground and the background. Furthermore, the AUCC is employed to achieve the count classification task, which is complementary to the task of density estimation. We evaluate our model on three public datasets: ShanghaiTech, UCF_CC_50 and Mall. Extensive experiments demonstrate that our counting model is on par with some state-of-the-art methods. Source code will be released at:https://github.com/wxq-ahu/crowd-count-amcnn.

人群计数是计算机视觉中的一个重要问题，计算机视觉的应用可以在许多任务中找到。尽管已经对该问题进行了深入研究，但是如何有效处理比例尺变化和透视图失真仍然是一个挑战。高质量的人群密度图在很大程度上取决于如何解决这些问题。在本文中，我们提出了一种新颖的网络架构，即用于人群计数的专心多阶段CNN（AMCNN）。AMCNN包含两个子网，即层次密度估计器（HDE）和辅助计数分类器（AUCC）。HDE采用分级策略以从粗到精的方式挖掘语义特征，以解决缩放比例变化和视角失真的问题。然后将获得的合成特征用于生成最终密度图。此外，为了进一步提高密度图的质量，将软注意力机制集成到AMCNN中，以区分前景和背景。此外，使用AUCC来完成计数分类任务，这与密度估计任务是互补的。我们在三个公共数据集上评估我们的模型：ShanghaiTech，UCF_CC_50和Mall。大量实验表明，我们的计数模型与某些最新方法相当。源代码将在以下位置发布：https：//github.com/wxq-ahu/crowd-count-amcnn。UCF_CC_50和购物中心。大量实验表明，我们的计数模型与某些最新方法相当。源代码将在以下位置发布：https：//github.com/wxq-ahu/crowd-count-amcnn。UCF_CC_50和购物中心。大量实验表明，我们的计数模型与某些最新方法相当。源代码将在以下位置发布：https：//github.com/wxq-ahu/crowd-count-amcnn。