Crowd counting is the challenging task in the crowd scene analysis. To tackle the scale variant issue and to calculate the more accurate result in this target task, this paper designs an effective crowd counting method based on multi-resolution context and image quality assessment-guided training. Specially, a multi-resolution context module is designed to extract the multi-scale context adaptively to enhance the final counting performance through learning the imbalance between different scale paths. An image quality assessment-guided training approach is developed to facilitate the crowd counting network to generate high-quality density map and more accurate counting result. Extensive experiments on benchmarks demonstrate the effectiveness of the proposed method on crowd counting task, the generalization of the proposed method, and the generalization of the developed image quality assessment-guided training approach.

人群计数是人群场景分析中的一项艰巨任务。为了解决尺度变异问题并在此目标任务中计算出更准确的结果，本文设计了一种基于多分辨率上下文和图像质量评估指导的训练的有效人群计数方法。特别地，设计了一个多分辨率上下文模块，以通过学习不同比例路径之间的不平衡性来自适应地提取多尺度上下文，以增强最终计数性能。开发了一种以图像质量评估为指导的训练方法，以方便人群计数网络生成高质量的密度图和更准确的计数结果。在基准上进行的大量实验证明了该方法在人群计数任务上的有效性，该方法的推广，