Wi-Monitor: Wi-Fi Channel State Information-Based Crowd Counting with Lightweight and Low-Cost IoT Devices

Abstract

Crowd counting is of great importance to many applications in various scenarios. Wi-Fi Channel State Information (CSI)-based crowd counting is a highly accurate privacy-conscious method. However, the problem with CSI-based crowd counting is the size and cost of the CSI collecting tool. Most studies benefiting from CSI collection use laptops with specific Network Interface Cards (NICs). The size and cost of the laptops restrict the practicability of such systems and limit active repositioning and mobility of the devices. This research aims to realize highly accurate CSI-based crowd counting using only one pair of lightweight and low-cost IoT devices. The devices are very agile and can easily be deployed even in space-limited environments. However, they have the disadvantage of poor data transportation compared to laptops. We compensate for this drawback by adjusting the deployment location, using multiple preprocessing methods depending on the situation, and standardizing the data for each subcarrier. We conducted evaluations of crowd counting in two representative scenarios. For the scenario of crowd sizes of 0, 1, 2, and 3 persons, when we used a weighted moving average (WMA) filter and phase sanitization as the preprocessing methods, the accuracy was 70.3%. When we used percentage of nonzero elements (PEM) and a moving average (MA) filter as the preprocessing methods, the accuracy was 84.6%. For the scenario of crowd sizes of 0, 5, 10, 15, and 20 persons, when we used a WMA filter and phase sanitization as the preprocessing methods, the accuracy was 76.5%. When we used PEM and a MA filter as the preprocessing methods, the accuracy was 75.9%. We found that the appropriate preprocessing method differs between the case of a small number of people and the case of a large number of people.