AuthorsF. Xiao, Z. Guo, Y. Ni, X. Xie, S. Maharjan and Y. Zhang
TitleArtificial Intelligence Empowered Mobile Sensing for Human Flow Detection
AfilliationCommunication Systems
Project(s)Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications
StatusPublished
Publication TypeJournal Article
Year of Publication2019
JournalIEEE Network Magazine
Volume33
Issue1
Pagination78-83
Date Published02/2019
PublisherIEEE
Abstract

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.

Citation Key26444