Passive Crowd Speed Estimation in Adjacent Regions With Minimal WiFi Sensing

TL;DR

This paper introduces a WiFi-based method for estimating crowd speeds in a region and its neighbors without requiring people to carry devices, validated through extensive indoor and outdoor experiments.

Contribution

It presents a novel mathematical model linking WiFi RSSI cross-correlation to pedestrian speeds, enabling speed estimation in WiFi-free adjacent regions.

Findings

Accurately estimates crowd speeds with NMSE of 0.18.

Works in both indoor and outdoor environments.

Effective in real-world settings like museums and stores.

Abstract

In this paper, we propose a methodology for estimating the crowd speed using WiFi devices without relying on people to carry any device. Our approach not only enables speed estimation in the region where WiFi links are, but also in the adjacent possibly WiFi-free regions. More specifically, we use a pair of WiFi links in one region, whose RSSI measurements are then used to estimate the crowd speed, not only in this region, but also in adjacent WiFi-free regions. We first prove how the cross-correlation and the probability of crossing the two links implicitly carry key information about the pedestrian speeds and develop a mathematical model to relate them to pedestrian speeds. We then validate our approach with 108 experiments, in both indoor and outdoor, where up to 10 people walk in two adjacent areas, with variety of speeds per region, showing that our framework can accurately estimate these speeds with only a pair of WiFi links in one region. For instance, the NMSE over all experiments is 0.18. We also evaluate our framework in a museum-type setting and estimate the popularity of different exhibits. We finally run experiments in an aisle in Costco, estimating key attributes of buyers' behaviors.