| 摘要: |
| 人员计数是对指定区域内人口数量进行统计或准确估计的一种方法，在许多应用中都发挥了重要作用，例如公共安全、人群控制和营销分析等.传统的基于视频流、电子标签的人员计数方法硬件成本过高，并且基于视频流的人员计数方法在光线不足或有遮挡物的情况下精度低、可靠性差.提出一种基于Wi-Fi感知的人员计数方法，该方法对Wi-Fi中信道状态信息（channel state information，简称CSI）进行重构，多子载波的CSI有效减少了多径效应的影响，利用解卷相位与线性变换的方法重构CSI，使得相位信息能以簇的形式集中，避免了原始相位分布范围过大、随机性过高的问题，基于Hampel滤波器去除了载波振幅的奇异数据，减少了环境噪声因素对于人员数量特征造成的干扰，保证了利用无线信号进行人员计数的精度和稳定性，最后利用SVM分类进行人员计数.实验结果表明，该方法的计数准确度达到了约95.8%，能够在室内环境下准确地识别出人员的数量. |
| 关键词: CSI 人数统计 相位重构 滤波去噪 支持向量机 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金（61702354，61876121）；苏州科技大学科研项目（XKZ2017004）；江苏省物联网移动互联技术工程重点实验室开放课题（JSWLW2017004）；研究生科研创新计划（SKSJ18_012，SJCX19_0963） |
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| Wi-Fi Perception Based Research on Personnel Counting Method |
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ZHOU Ze-Lun1, DAI Huan1,2, HUANG He2, SHI Wen-Hua1
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1.School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215000, China;2.School of Computer Science and Engineering, Soochow University, Suzhou 215000, China
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| Abstract: |
| Personnel counting are a method of counting or accurately estimating the population in a given area. It plays an important role in many applications, such as public safety, crowd control and marketing analysis. The traditional personnel counting method based on video stream and electronic tag has high hardware cost, and the accuracy and reliability of the personnel counting method based on video stream are low under the condition of insufficient light or occlusion. This paper presents a method of personnel counting based on Wi-Fi perception. This method reconstructs channel state information (CSI) in Wi-Fi. CSI of multi-subcarriers effectively reduces the influence of multipath effect. CSI is reconstructed by deconvolution phase and linear transformation, so that phase information can be centralized in the form of clusters. It avoids the problem of too large range of original phase distribution and too high randomness. Based on Hampel filter, the singular data of carrier amplitude is removed, the interference of environmental noise factors on the number of personnel characteristics is reduced, and the accuracy and stability of personnel counting using wireless signals are guaranteed. Finally, the numbers of people are classified by SVM. The experimental results show that the counting accuracy of the proposed method is about 95.8%, which can accurately identify the number of people in indoor environment. |
| Key words: channel state information population statistics phase reconstruction filtering and denoising support vector machine |
