School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China.
School of Cyber Security and Computer, Hebei University, Baoding 071000, China.
Sensors (Basel). 2022 Apr 22;22(9):3233. doi: 10.3390/s22093233.
In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting.
在本文中，我们提出了一种名为 CMSNet 的上下文感知多尺度聚合网络，用于密集人群计数，该网络有效地利用上下文信息和多尺度信息进行人群密度估计。为此，我们设计了一种上下文感知多尺度聚合模块（CMSM）。具体来说，CMSM 由多尺度聚合模块（MSAM）和上下文感知模块（CAM）组成。MSAM 用于获取多尺度人群特征。CAM 用于利用更多上下文信息增强提取的多尺度人群特征，从而有效地识别人群。我们在三个具有挑战性的数据集（即上海科技大学、UCF_CC_50 和 UCF-QNRF）上进行了广泛的实验，结果表明，我们的模型在拥挤人群计数方面的性能优于其他最先进的方法，这证明了我们的方法对于拥挤人群计数的有效性。
