计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 297-303.doi: 10.11896/jsjkx.191100506C

• 交叉与前沿 • 上一篇    下一篇

WiCount:一种基于WiFi-CSI的人数识别方法

丁亚三, 郭斌, 辛通, 王沛, 王柱, 於志文   

  1. (西北工业大学计算机学院 西安710072)
  • 收稿日期:2018-10-07 出版日期:2019-11-15 发布日期:2019-11-14
  • 通讯作者: 郭斌(1980-),男,博士,教授,CCF高级会员,主要研究方向为普适计算、移动群智感知,E-mail:guob@nwpu.edu.cn
  • 作者简介:丁亚三(1995-),男,硕士生,CCF会员,主要研究方向为无线感知;辛通(1993-),男,硕士生,主要研究方向为无线感知;王沛(1995-),男,硕士生,主要研究方向为无线感知;王柱(1983-),男,博士,副教授,CCF会员,主要研究方向为普适计算、社会网络分析;於志文(1977-),男,博士,教授,CCF高级会员,主要研究方向为普适计算、社会感知计算。
  • 基金资助:
    本文受国家自然科学基金(61772428,61725205),国家重点研发计划(2017YFB1001803)资助。

WiCount:A Crowd Counting Method Based on WiFi Channel State Information

DING Ya-san, GUO Bin, XIN Tong, WANG Pei, WANG Zhu, YU Zhi-wen   

  1. (School of Computer Science,Northwestern Polytechnical University,Xi’an 710072,China)
  • Received:2018-10-07 Online:2019-11-15 Published:2019-11-14

摘要: 人数识别即是对一定区域内活动人数的监测计数,在人群控制、流量监管等方面有着重要应用。例如,在百货商场或者机场中,对排队人数或者服务区休息人数进行估计可以为提升服务质量做出贡献。目前,研究人员已提出了一些基于摄像头和可穿戴设备的人数识别方法,但是这些方案均存在一些不足,例如摄像头只能提供可视范围内的监控,可穿戴设备需要被监控对象有意识地穿戴。也有一些学者利用雷达相关技术实现了穿墙式感知识别,但是这类系统设计复杂,应用成本较高,多用于军事领域。文中提出了一种基于WiFi信号的室内人数识别方案WiCount,其利用信道状态信息(Channel State Information,CSI)的幅值波动来刻画室内人数的变化,利用机器学习算法实现对人的计数。WiCount旨在进行更细粒度的室内人数识别,即人在室内任意位置时该方法均能准确识别人数。它根据室内人数与CSI幅值变化的关系,提取了有效的数学特征,减弱了相同人数在室内不同位置所产生的CSI幅值波动差异,然后通过训练3种分类器(SVM、KNN、BP神经网络)来识别监测区域内的人数。在实验室和会议室分别部署了验证系统,结果显示,在人数规模较小的情况下,所提方法的识别效果良好。其中,实验室环境下,不超过4人时,系统的识别率达90%;会议室环境下,不超过2人,在监测区域内任意位置活动时,系统的识别率可达89.58%。

关键词: WiFi感知, 机器学习, 人数识别, 无线感知, 信道状态信息

Abstract: Crowd counting is the process of monitoring the number of people in a certain area,which is crucial in traffic supervision,etc.For example,counting people waiting in lines at airports or retail stores could be used for improving the service.At present,some methods based on videos (or images) and wearable devices have been proposed,but there are some shortcomings in these schemes.For example,the camera can only monitor within the range of sight distance,and wearable devices need people to wear them consciously.Some scholars have made use of radar related technology torealize the number,but its cost is very high.In this paper,an indoor crowd counting scheme,WiCount,based on WiFi signals was proposed.WiCount aims at a fine-grained indoor people counting scheme,which can accurately identify the number of people at different positions.According to the relationship between the number of indoor people and the amplitudes fluctuation of CSI,features are extracted,which are contributed to mitigate the difference of CSI data produced by the same number of people in distinct positions,and then three classifiers (SVM,KNN,BP Neural Network) are trained to identify the number of people in the monitoring area.Prototype systems is implemented in a laboratory and a meeting room respectively,and the recognition is fine when the number of people is on the small side.In the laboratory,the accuracy is up to 90% in the case of no more than 4 persons.In the meeting room,the results show that no matter where people move,the accuracy can reach 89.58% in the case of no more than 2 persons.

Key words: Channel state information, Crowd counting, Machine learning, WiFi sensing, Wireless sensing

中图分类号: 

  • TP399
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