Crowd counting, i.e., estimating the number of people in crowded areas, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast density variations and severe occlusion within the interested crowd area. In this paper, we propose a novel Pyramid Density-Aware Attention based network, abbreviated as PDANet, which leverages the attention, pyramid scale feature, and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract features of different scales while focusing on the relevant information and suppressing the misleading information. We also address the variation of crowdedness levels among different images with a Density-Aware Decoder (DAD) modules. For this purpose, a classifier is constructed to evaluate the density level of the input features and then passes them to the corresponding high and low density DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowdedness density maps. Meanwhile, we employ different losses aiming to achieve a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state-of-the-art approaches.

人群计数，即估计拥挤区域的人数，引起了研究界的极大兴趣。尽管已进行了许多尝试，但由于感兴趣的人群区域内的巨大密度变化和严重的遮挡，人群计数仍然是一个开放的现实世界问题。在本文中，我们提出了一个新颖的基于金字塔密度感知的注意力网络，简称为PDANet，它利用注意力，金字塔尺度特征和两个分支解码器模块进行了密度感知的人群计数。PDANet利用这些模块提取不同比例尺的特征，同时关注相关信息并消除误导性信息。我们还使用密度感知解码器（DAD）模块解决了不同图像之间拥挤程度的变化。以此目的，构建分类器以评估输入要素的密度级别，然后将它们传递到相应的高密度和低密度DAD模块。最后，我们通过考虑低拥挤密度图和高拥挤密度图的总和来生成总体密度图。同时，我们采用了不同的损耗，目的是为输入场景获得精确的密度图。在具有挑战性的基准数据集上进行的广泛评估很好地证明了所提出的PDANet在计数和生成的密度图的准确性方面优于众所周知的最新方法的优越性能。我们采用了不同的损耗，旨在为输入场景获得精确的密度图。在具有挑战性的基准数据集上进行的广泛评估很好地证明了所提出的PDANet在计数和生成的密度图的准确性方面优于众所周知的最新方法的优越性能。我们采用了不同的损耗，旨在为输入场景获得精确的密度图。在具有挑战性的基准数据集上进行的广泛评估很好地证明了所提出的PDANet在计数和生成的密度图的准确性方面优于众所周知的最新方法的优越性能。