Simultaneous Crowd Estimation in Counting and Localization Using WiFi CSI

In the field of crowd estimation, most non-visual approaches confine their objective to only crowd counting, whereas there are a number of vision-based researches which can estimate both the number and location of people. By observation, we figured out that the WiFi channel state information (CSI) also contains the potential characteristics for both estimations. In this paper, we propose a user-device-free simultaneous crowd estimation system that enables both crowd counting and localization simultaneously, by WiFi CSI and Machine Learning. The originality of this study is that we leverage the CSI bundles as the source for extracting features that contain characteristics depending on the dynamic state (counting) and static state (localization). By experiments during three different-time sessions, we confirm that we could achieve up to 94% counting accuracy and 95% localization accuracy by k-fold cross-validation.