Tracking human queues using single-point signal monitoring

Abstract

We investigate using smartphone WiFi signals to track human queues, which are common in many business areas such as retail stores, airports, and theme parks. Real-time monitoring of such queues would enable a wealth of new applications, such as bottleneck analysis, shift assignments, and dynamic workflow scheduling. We take a minimum infrastructure approach and thus utilize a single monitor placed close to the service area along with transmitting phones. Our strategy extracts unique features embedded in signal traces to infer the critical time points when a person reaches the head of the queue and finishes service, and from these inferences we derive a person's waiting and service times. We develop two approaches in our system, one is directly feature-driven and the second uses a simple Bayesian network. Extensive experiments conducted both in the laboratory as well as in two public facilities demonstrate that our system is robust to real-world environments. We show that in spite of noisy signal readings, our methods can measure service and waiting times to within a 10 second resolution.

LineKing: Coffee Shop Wait-Time Monitoring Using Smartphones

“…As shown in the figure, almost 85% of the errors are less than 1 min. In comparison to previous work [5], which reportedly has 10 sec. mean error, we believe that our variance-based classifier is accurate enough for our end goal of estimating the waittime for newly arrived customers without requiring to place special equipments in the coffee shop.…”

“…Line wait-time detection and estimation. Traditional solutions to the line wait-time monitoring problem are based on infrastructure-based solutions such as camera placement [1], [2], sensor deployment [3] or monitoring signals generated through bluetooth [4] or Wi-Fi capable devices [5]. However, these solutions usually do not scale well as they are costly to deploy and mostly designed for specific places.…”

“…Also most of the time, extensive training and testing are required before real-world deployment. Among these, we identified [5] as the most relevant work to ours. In [5], authors monitor the received signal strength of the smartphones using a specially placed WAP.…”

“…Among these, we identified [5] as the most relevant work to ours. In [5], authors monitor the received signal strength of the smartphones using a specially placed WAP. Using the signal strength pattern resulted from user's movement, they try to identify the wait-time of the user.…”

Immediate Cooperative Line Wait Time Estimation System Using BLE on Smartphone

“…Our system utilizes BLE broadcasting by smartphones; thus, there is no requirement for these processes between smartphones and a server. In [5], Wang et al described a tracking human queues system using single-point Wi-Fi signal monitoring. They define a three periods in a human queue: waiting, service and leaving periods.…”