Pedestrian Flow Estimation Through Passive WiFi Sensing
Pedestrian Flow Estimation Through Passive WiFi Sensing
复制标题
通过无源 WiFi 传感进行行人流量估算
DOI：
10.1109/tmc.2019.2959610
复制
发表时间：
2021
影响因子：
7.9
通讯作者：
Yun Wei
中科院分区：
文献类型：
--
作者：
Baoqi Huang;Guoqiang Mao;Yong Qin;Yun Wei
在公共场所，即使行人的移动设备没有连接任何WiFi接入点（AP），也会广播WiFi探测请求，以便WiFi嗅探器将这些WiFi探测数据包众包使用。本文研究了利用被动WiFi传感方法进行行人流量分析的问题。首先，通过对WiFi嗅探器与移动行人流相互作用的概率分析，建立被动WiFi感知模型，捕捉影响行人流特征的主要因素。在此基础上，提出了一种基于rao - blackwell化粒子滤波（RBPF）的序贯滤波算法，利用实时嗅闻结果对行人流速度和行人数量进行同时有效的估计。为了验证这一研究，在中国广州的一个地铁站换乘通道部署了一个使用WiFi嗅探器的实验性行人监控系统。通过大量的实验验证了被动感知模型，验证了所提算法的有效性和优越性。行人流量估计不仅有助于改善安全、设施管理和客户服务，而且为引入其他新颖的应用铺平了道路。
In public places, even if pedestrians do not have their mobile devices connected with any WiFi access point (AP), WiFi probe requests will be broadcast, so that WiFi sniffers can be employed to crowdsource these WiFi probe packets for use. This paper tackles the problem of exploiting the passive WiFi sensing approach for pedestrian flow analysis. To be specific, a passive WiFi sensing model is first established based on a probabilistic analysis of interactions between WiFi sniffers and the moving pedestrian flow, capturing the main factors affecting pedestrian flow characteristics. On that basis, a sequential filtering algorithm is proposed based on the Rao-Blackwellized particle filter (RBPF) to produce simultaneous and efficient estimates of the pedestrian flow speed and pedestrian number utilizing the real-time sniffing results. In order to validate this study, an experimental pedestrian surveillance system using WiFi sniffers is deployed at the transfer channel of a metro station in Guangzhou, China. Extensive experiments are conducted to verify the passive sensing model, and confirm the effectiveness and advantages of the proposed algorithm. The pedestrian flow estimation not only helps to improve the safety and facility management and customer services, but also paves the way for introducing other novel applications.