Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum

Brena, Ramon F.; Escudero, Edgar; Vargas-Rosales, Cesar; Galvan-Tejada, Carlos E.; Munoz, David

doi:10.3390/electronics10030315

Open AccessArticle

Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum

by

Ramon F. Brena

^1,*

,

Edgar Escudero

^1,2,

Cesar Vargas-Rosales

¹

,

Carlos E. Galvan-Tejada

³

and

David Munoz

¹

Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Nuevo León, Mexico

²

Aerobit Technologies, Av. Eugenio Garza Sada 3820, Monterrey 64780, Nuevo León, Mexico

³

Unidad Académica de Ingeniería Eléctrica y Comunicaciones, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas Centro 98000, Zacatecas, Mexico

^*

Author to whom correspondence should be addressed.

Electronics 2021, 10(3), 315; https://doi.org/10.3390/electronics10030315

Submission received: 19 December 2020 / Revised: 18 January 2021 / Accepted: 22 January 2021 / Published: 29 January 2021

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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Abstract

Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s presence, so by identifying these distortion patterns, we can estimate the number of people in such a given space. In this work, we refine methods that leverage Channel State Information (CSI), which is used to train a classifier that estimates the number of people placed between a Wi-Fi transmitter and a receiver, and we show that the available multi-link information allows us to achieve substantially better results than state-of-the-art single link or averaging approaches, that is, those that take the average of the information of all channels instead of taking them individually. We show experimentally how the addition of each of the multiple links information helps to improve the accuracy of the prediction from 44% with one link to 99% with 6 links.

Keywords: Wi-Fi; CSI; crowd counting; Doppler spectrum

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MDPI and ACS Style

Brena, R.F.; Escudero, E.; Vargas-Rosales, C.; Galvan-Tejada, C.E.; Munoz, D. Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum. Electronics 2021, 10, 315. https://doi.org/10.3390/electronics10030315

AMA Style

Brena RF, Escudero E, Vargas-Rosales C, Galvan-Tejada CE, Munoz D. Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum. Electronics. 2021; 10(3):315. https://doi.org/10.3390/electronics10030315

Chicago/Turabian Style

Brena, Ramon F., Edgar Escudero, Cesar Vargas-Rosales, Carlos E. Galvan-Tejada, and David Munoz. 2021. "Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum" Electronics 10, no. 3: 315. https://doi.org/10.3390/electronics10030315

APA Style

Brena, R. F., Escudero, E., Vargas-Rosales, C., Galvan-Tejada, C. E., & Munoz, D. (2021). Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum. Electronics, 10(3), 315. https://doi.org/10.3390/electronics10030315

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum

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