The current crowd counting tasks rely on a fully convolutional network to generate a density map that can achieve good performance. However, due to the crowd occlusion and perspective distortion in the image, the directly generated density map usually neglects the scale information and spatial contact information. To solve it, we proposed MDPDNet (Multiresolution Density maps and Parallel Dilated convolutions’ Network) to reduce the influence of occlusion and distortion on crowd estimation. This network is composed of two modules: (1) the parallel dilated convolution module (PDM) that combines three dilated convolutions in parallel to obtain the deep features on the larger receptive field with fewer parameters while reducing the loss of multiscale information; (2) the multiresolution density map module (MDM) that contains three-branch networks for extracting spatial contact information on three different low-resolution density maps as the feature input of the final crowd density map. Experiments show that MDPDNet achieved excellent results on three mainstream datasets (ShanghaiTech, UCF_CC_50, and UCF-QNRF).

中文翻译：

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基于多分辨率密度图和并行膨胀卷积的人群计数

当前的人群计数任务依赖于全卷积网络来生成可以实现良好性能的密度图。然而，由于图像中的人群遮挡和透视变形，直接生成的密度图通常会忽略比例信息和空间接触信息。为了解决这个问题，我们提出了MDPDNet（多分辨率密度图和平行扩张卷积网络），以减少遮挡和失真对人群估计的影响。该网络由两个模块组成：（1）并行扩展卷积模块（PDM），它并行组合三个扩展卷积，以较少的参数获得较大接收场上的深层特征，同时减少了多尺度信息的丢失；（2）多分辨率密度图模块（MDM），该模块包含三个分支网络，用于提取三个不同的低分辨率密度图上的空间联系信息，作为最终人群密度图的特征输入。实验表明，MDPDNet在三个主流数据集（ShanghaiTech，UCF_CC_50和UCF-QNRF）上均取得了优异的结果。