Overhead-Free People Counting in mmWave Network Using IEEE 802.11bf Passive Sensing

Published

October 17, 2024

Author(s)

Tanguy Ropitault, Anirudha Sahoo, Steve Blandino, Nada Golmie

Abstract

Accurately assessing the number of people in a room is essential for enhancing operational efficiency, safety, and sustainability, enabling applications such as smart building management, energy conservation, and emergency evacuation planning. Wi-Fi sensing, favored for its privacy-preserving ca-pabilities and the ubiquity of Wi-Fi infrastructure, has become a popular method for such sensing tasks, including people counting. Within this context, our paper introduces a Convolutional Neural Network (CNN) model for millimeter wave (mmWave) Wi-Fi people counting, capitalizing on the IEEE 802.11bf amendment, and in particular its passive sensing framework. By evaluating Range-Doppler (RD), Azimuth-Doppler (AD), and Azimuth- Range (AR) map representations as inputs, we establish AR maps' superiority, achieving up to 98.57% accuracy for counting up to four individuals. Our solution, free of communication overhead, also minimizes energy consumption and computational requirements, offering a scalable and efficient sensing solution for people counting, particularly well-suited for Internet of Things (IoT) devices with limited resources.

Proceedings Title

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

Conference Dates

September 2-5, 2024

Conference Location

Kauna, HI, US

Conference Title

PIMRC 2025

Pub Type

Conferences

Download Paper

Local Download

Keywords

Wi-Fi Sensing, mmWave, IEEE 802.11bf

Citation

Ropitault, T. , Sahoo, A. , Blandino, S. and Golmie, N. (2024), Overhead-Free People Counting in mmWave Network Using IEEE 802.11bf Passive Sensing, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Kauna, HI, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957538 (Accessed October 6, 2025)