FreeCount: Device-Free Crowd Counting with Commodity WiFi

Abstract

In the era of Internet of Things, crowd counting, which estimates the number of people within a region, becomes the underpinning for many emerging applications, such as occupancy estimation in smart building and queuing management and product placement in shopping center. Existing vision based crowd counting schemes require favorable lighting conditions and also raise privacy concerns. RF based approaches rely on specialized sensors and require users to carry RF devices. Thus, an accurate, reliable and non-intrusive crowd counting scheme is still desired. In this paper, we propose FreeCount, a device-free crowd counting scheme that is able to precisely estimate the number of people within a region using only commodity WiFi routers. To this end, the channel state information (CSI) data in PHY layer is obtained directly by upgrading the router's software. We propose an information theory based feature selection scheme to select the most representative features that are sensitive to human motion. To build a classifier that is robust to temporal and environmental disparities, we adopt transfer kernel learning, which minimizes the difference between the source and target distributions in the reproducing kernel Hilbert space, is adopted to process the real-time CSI feature data. Experiments were conducted in moderate sized rooms and the results demonstrated that FreeCount is able to accurately estimate the number of people with 96% crowd counting accuracy consistently over temporal and environmental variation.