Passive Crowd Speed Estimation in Adjacent Regions With Minimal WiFi Sensing

In this paper, we propose a methodology for estimating the crowd speed using WiFi devices, and without relying on people to carry any device (passively). Our approach not only enables speed estimation in the region where WiFi links are, but also in the adjacent possibly WiFi-free regions where there may be no WiFi signal available. 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 of 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 a 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. Furthermore, the overall classification accuracy, when crowd speed is categorized as slow, normal, and fast, is 85%. We also evaluate our framework in a museum-type setting, where two exhibitions showcase two different types of displays. We show how our methodology can estimate the visitor speeds in both exhibits, deducing which exhibit is more popular. We finally run experiments in an aisle in Costco, estimating key attributes of buyers’ behaviors.