Device-free intruder sensing leveraging fine-grained physical layer signatures

Abstract

With the development of smart indoor spaces, intruder sensing has attracted great attention in the past decades. Realtime intruder sensing in intelligent video surveillance is challenging due to the various covariate factors such as walking surface, clothing, carrying condition. Gait recognition provides a feasible approach for human identification. Pioneer systems usually rely on computer vision or wearable sensors which pose unacceptable privacy risks or be limited to additional devices. In this paper, we present CareFi, a device-free intruder sensing system that can identify a stranger or a burglar based on Commercial Off-The-Shelf (COTS) WiFi-enabled devices. CareFi extracts the fine-grained physical layer Channel State Information (CSI) to analyze the distinguishing gait characteristics for intruder sensing. CareFi can identify the intruder under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. CareFi does not require any dedicated sensors or lighting and works in dark just as well as in light. We prototype CareFi using commercial off-the-shelf WiFi devices and experimental results in typical indoor scenarios show that it achieves more than 87.2\% detection rate for intruder sensing.