Are You in the Line? RSSI-based Queue Detection in Crowds

TL;DR

This paper presents a novel RSSI-based system for detecting if a device is in a queue, leveraging multi-device and multi-sensor features, outperforming camera-based methods in real-world scenarios.

Contribution

It introduces a plug-and-play queue detection system using Wi-Fi/BLE RSSI signals, with new feature extraction methods from multiple devices and sensors for improved accuracy.

Findings

Detection accuracy reaches at least 77%.

Wireless signal-based detection outperforms camera-based face detection.

System works effectively in real-world social events.

Abstract

Crowd behaviour analytics focuses on behavioural characteristics of groups of people instead of individuals' activities. This work considers human queuing behaviour which is a specific crowd behavior of groups. We design a plug-and-play system solution to the queue detection problem based on Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs) captured by multiple signal sniffers. The goal of this work is to determine if a device is in the queue based on only RSSIs. The key idea is to extract features not only from individual device's data but also mobility similarity between data from multiple devices and mobility correlation observed by multiple sniffers. Thus, we propose single-device feature extraction, cross-device feature extraction, and cross-sniffer feature extraction for model training and classification. We systematically conduct experiments with simulated queue movements to study the detection accuracy. Finally, we compare our signal-based approach against camera-based face detection approach in a real-world social event with a real human queue. The experimental results indicate that our approach can reach minimum accuracy of 77% and it significantly outperforms the camera-based face detection because people block each other's visibility whereas wireless signals can be detected without blocking.