Crowd Counting Through Walls Using WiFi

TL;DR

This paper introduces a WiFi-based method to count people inside buildings from outside by analyzing inter-event times of received signals, demonstrating high accuracy across various environments and wall materials.

Contribution

The paper presents a novel approach that uses inter-event times of WiFi RSSI measurements and renewal process theory to estimate the number of people behind walls without prior calibration.

Findings

Accurately estimates people count behind walls in diverse environments.

Robust to different wall materials like concrete, plaster, and wood.

Validated with 44 experiments involving up to 20 people.

Abstract

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.