Low-cost Pedestrian Counter Using Wi-Fi APs for Smart Building Applications

Understanding people flows in indoor environments such as office buildings and shopping malls is becoming more significant since such information can be useful for smart building applications. For example, crowd navigation applications can be designed for those people with big baggage to choose less crowded routes. People density and flow information is also useful for more adaptive and flexible shop advertisement. Wi-Fi RSSI-based passive measurement is a cost-effective approach as it allows to involve already-installed Wi-Fi APs as a part of the system. Nevertheless, the existing approaches have not tackled the high mobility cases where pedestrians walk with relatively high speed. In this paper, we propose a method to count the number of pedestrians waking across the line between two Wi-Fi devices. The method focuses on the RSSI fluctuations seen when pedestrians pass the device line. We extract some feature values that represent such attenuation and variance of RSSI and build a multi-class classifier to identify the number of pedestrians. The experiments were conducted with different position arrangements of Wi-Fi devices and at different locations to assess the applicability of the method. The results indicate that the proposed method can estimate the number of pedestrians with high accuracy when the Wi-Fi devices are placed obliquely against the walking direction.