CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection Using Wi-Fi CSI

In recent years, the demand for pervasive smart services and applications has increased rapidly. Device-free human detection through sensors or cameras has been widely adopted, but it comes with privacy issues as well as misdetection for motionless people. To address these drawbacks, channel state information (CSI) captured from commercialized Wi-Fi devices provides rich signal features for accurate detection. However, existing systems suffer from inaccurate classification under a Nonline-of-Sight (NLoS) and stationary scenario, such as when a person is standing still in a room corner. In this work, we propose a system called colorization and contrastive learning enhanced NLoS human presence detection (CRONOS), which generates dynamic recurrence plots (RPs) and color-coded CSI ratios to distinguish mobile and stationary people from vacancy in a room, respectively. We also incorporate supervised contrastive learning to retrieve substantial representations, where consultation loss is formulated to differentiate the representative distances between dynamic and stationary cases. Furthermore, we propose a self-switched static feature-enhanced classifier (S3FEC) to determine the utilization of either RPs or color-coded CSI ratios. Our comprehensive experimental results show that CRONOS outperforms existing systems that either apply machine learning or nonlearning-based methods, as well as non-CSI-based features in the open literature. CRONOS achieves the highest human presence detection accuracy in vacancy, mobility, Line-of-Sight (LoS), and NLoS scenarios.