Human Detection For Crowd Count Estimation Using CSI of WiFi Signals

Abstract

We address the problem of crowd estimation in situations such as indoor events using anonymous and non-participatory CSI of WiFi Signals. Observing the great resemblance of Channel State Information (CSI, a finegrained information captured from the received Wi-Fi signal) to texture, we propose a brand-new framework based on statistical mechanics, and relying only on sets of machine learning techniques.In this paper, a framework for crowd count estimation is presented which utilizes Chebyshev filter and SVD to remove background noise in the CSI data, PCA to reduce the dimensionality of the CSI data and spectral descriptors for feature extraction. From the extracted feature, a set of classiffying algorithms are then utilised for training and testing the accuracy of our crowd estimation framework The aim of this framework to effectively and efficiently extract the channel information in WiFi signals across OFDM carriers reflected by the presence of human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed framework. Our result depict that our estimation becomes more–rather than less–accurate when the crowd count increases.