Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

TL;DR

This paper introduces a privacy-preserving crowd monitoring system using passive WiFi probe request detection, validated through real-world data and theoretical analysis, suitable for large public spaces.

Contribution

It presents a novel crowd counting system that ensures privacy, with a mathematical model, efficiency analysis, and empirical validation against camera-based counts.

Findings

System accurately estimates crowd size in public spaces.

Theoretical model provides bounds on counting accuracy.

System maintains privacy while monitoring large crowds.

Abstract

This paper presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundreds of attendees. We present our counting process and an associated mathematical model. From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator. Then, we describe our system. We present and discuss our sensor hardware, our computing system architecture, and an efficient implementation of our counting algorithm -- as well as its space and time complexity. We also show how our system ensures the privacy of people in the monitored area. Finally, we validate our system using nine weeks of data from a public library endowed with a camera-based counting system, which generates counts against which we compare those of our counting system. This comparison empirically quantifies the accuracy of our counting system, thereby showing it to be suitable for monitoring public areas. Similarly, the concentration inequality provides a theoretical validation of the system.