Crowd counting is an important surveillance application and receives significant attention from the computer vision community. Most of the current methods treat crowd counting by density map estimation and use the Fully Convolution Network (FCN) for prediction. The mainstream framework is to predict density maps and use the sum up the density maps to get the number of people. In such methods, the main drawback is the poor local quality of the dense part and the sparse part of an image. As we investigated, it is due to the lack of an efficient method to learn the heads' structure information. To address the above problem, in this paper, we propose a domain adaptive model called synthetic guided learning that learns features' structure from synthetic data. We also propose a multi-scale edge-aware loss for improving the boundary clearness of the estimated density map. Our experimental results show, learning from the structure information effectively improves the density maps' estimation quality and promotes the counting accuracy. Comprehensive experiments and comparisons with state-of-the-art methods on four publicly available data sets demonstrate the superiority of our proposed method. We provide a reference implementation of this technique at https://github.com/MRJTM/SGEANet.

人群计数是重要的监视应用程序，受到了计算机视觉界的广泛关注。当前大多数方法通过密度图估计来处理人群计数，并使用完全卷积网络（FCN）进行预测。主流框架是预测密度图，并使用密度图的总和来获得人数。在这种方法中，主要缺点是图像的密集部分和稀疏部分的局部质量差。正如我们调查的那样，这是由于缺乏一种有效的方法来学习头部的结构信息。为了解决上述问题，在本文中，我们提出了一种称为自适应制导学习的领域自适应模型，该模型从合成数据中学习特征的结构。我们还提出了一种多尺度边缘感知损失，以提高估计密度图的边界清晰度。我们的实验结果表明，从结构信息中学习可以有效地提高密度图的估计质量并提高计数精度。在四个公开可用的数据集上进行的综合实验和最新技术方法的比较证明了我们提出的方法的优越性。我们在以下位置提供了此技术的参考实现https://github.com/MRJTM/SGEANet。