Crowd counting, i.e., estimating the number of people in a crowded area, 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 scale variations in crowd density within the interested area, and severe occlusion among the crowd. In this paper, we propose a novel Pyramid Density-Aware Attention-based network, abbreviated as PDANet, that leverages the attention, pyramid scale feature and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract different scale features, focus on the relevant information, and suppress the misleading ones. We also address the variation of crowdedness levels among different images with an exclusive Density-Aware Decoder (DAD). For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowded density maps as spatial attention. Meanwhile, we employ two losses to create 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 arts.

中文翻译：

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PDANet：用于准确人群计数的金字塔密度感知注意网络

人群计数，即估计拥挤区域的人数，已经引起了研究界的极大兴趣。尽管已经报道了许多尝试，但由于感兴趣区域内人群密度的巨大规模变化以及人群之间的严重遮挡，人群计数仍然是一个开放的现实世界问题。在本文中，我们提出了一种新颖的基于金字塔密度感知注意力的网络，缩写为 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密度感知人群计数。PDANet 利用这些模块提取不同尺度的特征，关注相关信息，抑制误导信息。我们还使用独特的密度感知解码器 (DAD) 解决了不同图像之间拥挤程度的变化。以此目的，分类器评估输入特征的密度水平，然后将它们传递给相应的高低拥挤 DAD 模块。最后，我们通过将低密度图和高密度图的总和视为空间注意力来生成整体密度图。同时，我们采用两个损失来为输入场景创建精确的密度图。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 在计数和生成密度图的准确性方面的卓越性能，超过了众所周知的现有技术。我们采用两个损失来为输入场景创建精确的密度图。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 在计数和生成密度图的准确性方面的卓越性能，超过了众所周知的现有技术。我们采用两个损失来为输入场景创建精确的密度图。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 在计数和生成密度图的准确性方面的卓越性能，超过了众所周知的现有技术。