Intelligent human detection based on WiFi is a technique that has recently attracted a significant amount of interest from research communities. The use of ubiquitous WiFi to detect the number of queuing persons can facilitate dynamic planning and appropriate service provisioning. In this article, we propose HFD, one of the first schemes to leverage WiFi signals to estimate the number of queuing persons by employing classifiers from machine learning in a device-free manner. In the proposed HFD scheme, we first utilize the sliding window method to filter and remove the outliers. We extract two characteristics, skewness and kurtosis, as the identification features. Then, we use the support vector machine (SVM) to classify these two features to estimate the number of people in the current queue. Finally, we combine our scheme with the latest Fresnel Zone model theory to determine whether someone is in or out, and thus dynamically adjust the detected value. We implement a proof-of-concept prototype upon commercial WiFi devices and evaluate it in both conference room and corridor scenarios. The experimental results show that the accuracy of our proposed HFD detection can be maintained at about 90 percent with high robustness.

中文翻译：

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人工智能增强了移动感应的人体流量检测能力

基于WiFi的智能人体检测是一种最近引起研究界极大兴趣的技术。使用无处不在的WiFi来检测排队人数可以促进动态规划和适当的服务供应。在本文中，我们提出了HFD，这是第一种利用WiFi信号以无设备方式使用来自机器学习的分类器来估计排队人数的方案。在提出的HFD方案中，我们首先利用滑动窗口方法来过滤和去除异常值。我们提取偏斜度和峰度这两个特征作为识别特征。然后，我们使用支持向量机（SVM）对这两个功能进行分类，以估计当前队列中的人数。最后，我们将方案与最新的菲涅耳区模型理论结合起来，确定有人进出还是进出，从而动态调整检测到的值。我们在商用WiFi设备上实施了概念验证原型，并在会议室和走廊场景中对其进行了评估。实验结果表明，我们提出的HFD检测的准确性可以保持在90％左右，并且具有很高的鲁棒性。