Crowd counting plays an important role in crowd analysis and monitoring. To this end, we propose a novel method called Adaptive Weighted Crowd Receptive Field Network (AWRFN) for crowd counting to estimate the number of people and the spatial distribution of input crowd images. The proposed AWRFN is composed of four modules: backbone, crowd receptive field block (CRFB), recurrent block (RB), and channel attention block (CAB). Backbone utilizes the first ten layers of VGG16 to extract base features of input images. CRFB is a multi-branch architecture simulating a real human visual system for further obtaining refined and discriminative crowd features. RB generates strong semantic and global information by recurrently stacking convolutional layers with the same parameters. CAB outputs appropriate weights to supervise each channel of the feature maps output from CRFB, which uses the outputs of RB as guidance. Different from previous works using Euclidean Loss, we employ L1_Smooth Loss to train our network in an end-to-end fashion. To demonstrate the effectiveness of our proposed method, we implement AWRFN on two representative datasets including the ShanghaiTech dataset and the UCF_CC_50 dataset. The experimental results prove that our method is both effective and robust compared with the state-of-the-art approaches.

人群计数在人群分析和监控中起着重要作用。为此，我们提出了一种称为自适应加权人群接收现场网络（AWRFN）的新颖方法，用于人群计数，以估计人数和输入人群图像的空间分布。拟议的AWRFN由四个模块组成：骨干，人群接受场块（CRFB），循环块（RB）和频道关注块（CAB）。骨干网利用VGG16的前十层来提取输入图像的基本特征。CRFB是一个模拟真实人类视觉系统的多分支体系结构，用于进一步获得精致的和具有区别性的人群特征。RB通过循环堆叠具有相同参数的卷积层来生成强大的语义和全局信息。CAB输出适当的权重，以监督从CRFB输出的特征图的每个通道，CRFB使用RB的输出作为指导。与以前使用欧几里得损失的工作不同，我们使用L1_Smooth Loss以端到端的方式训练我们的网络。为了证明我们提出的方法的有效性，我们对两个代表性数据集（包括ShanghaiTech数据集和UCF_CC_50数据集）实施了AWRFN。实验结果证明，与最新方法相比，我们的方法既有效又可靠。我们对两个代表性数据集（包括ShanghaiTech数据集和UCF_CC_50数据集）实施AWRFN。实验结果证明，与最新方法相比，我们的方法既有效又可靠。我们对两个代表性数据集（包括ShanghaiTech数据集和UCF_CC_50数据集）实施AWRFN。实验结果证明，与最新方法相比，我们的方法既有效又可靠。