Crowd counting is a conspicuous task in computer vision owing to scale variations, perspective distortions, and complex backgrounds. Existing research usually adopts the dilated convolution network to enlarge the receptive fields to solve the problem of scale variations. However, these methods easily bring background information into the large receptive fields to generate poor quality density maps. To address this problem, we propose a novel backbone called Context-guided Dense Attentional Dilated Network (CDADNet). CDADNet contains three components: an attentional module, a context-guided module and a dense attentional dilated module. The attentional module is used to provide attention maps which can remove background information, while the context-guided module is proposed to extract multi-scale contextual information. Moreover, the dense attentional dilated module aims to generate high-granularity density maps and the cascaded strategy is used to preserve information from changing scales. To verify the feasibility of our method, we compare it to the existing approaches on five crowd counting datasets (ShanghaiTech (Part_A and Part_B), WorldEXPO’10, UCSD, UCF_CC_50). The comparison results demonstrate that CDADNet is effective and robust for various scenes.

由于尺度变化、透视失真和复杂背景，人群计数是计算机视觉中的一项引人注目的任务。现有研究通常采用扩张卷积网络来扩大感受野来解决尺度变化的问题。然而，这些方法很容易将背景信息带入大的感受野以生成质量较差的密度图。为了解决这个问题，我们提出了一种称为上下文引导密集注意扩张网络（CDADNet）的新型主干。CDADNet 包含三个组件：注意力模块、上下文引导模块和密集注意力扩张模块。注意模块用于提供可以去除背景信息的注意力图，而上下文引导模块用于提取多尺度上下文信息。而且，密集注意力扩张模块旨在生成高粒度密度图，级联策略用于保护信息不受尺度变化的影响。为了验证我们方法的可行性，我们将其与五个人群计数数据集（ShanghaiTech（Part_A 和 Part_B）、WorldEXPO'10、UCSD、UCF_CC_50）上的现有方法进行了比较。比较结果表明 CDADNet 对各种场景都是有效且稳健的。