WiCross: Indoor Human Zone-Crossing Detection Using Commodity WiFi Devices

TL;DR

WiCross tackles privacy-preserving indoor doorway crossing detection using commodity WiFi devices. It exploits phase statistics of WiFi CSI within a diffraction-sensing framework, modeling the channel as $H = H_{LoS} + H_{Target}$ and leveraging a linear relationship between phase and the sum of transmitter-receiver distances to distinguish crossing from turning-back without user calibration. The system architecture—data acquisition, denoising, CFO/SFO mitigation, pattern extraction, and a local-maximum-based crossing detector—enables training-free operation. Empirical results on 816 trials show accuracy above 0.95 with false alarm rates below 5%, indicating strong practical potential for applications in smart homes for security and people counting. The work highlights the viability of phase-based WiFi sensing for accurate, privacy-friendly indoor event detection without specialized hardware or per-user training.

Abstract

Detecting whether a target crosses the given zone (e.g., a door) can enable various practical applications in smart homes, including intelligent security and people counting. The traditional infrared-based approach only covers a line and can be easily cracked. In contrast, reusing the ubiquitous WiFi devices deployed in homes has the potential to cover a larger area of interest as WiFi signals are scattered throughout the entire space. By detecting the walking direction (i.e., approaching and moving away) with WiFi signal strength change, existing work can identify the behavior of crossing between WiFi transceiver pair. However, this method mistakenly classifies the turn-back behavior as crossing behavior, resulting in a high false alarm rate. In this paper, we propose WiCross, which can accurately distinguish the turn-back behavior with the phase statistics pattern of WiFi signals and thus robustly identify whether the target crosses the area between the WiFi transceiver pair. We implement WiCross with commercial WiFi devices and extensive experiments demonstrate that WiCross can achieve an accuracy higher than 95\% with a false alarm rate of less than 5%.

Figures (6)

• Figure 1: Experimental setup
• Figure 2: Illustration of wireless signals' propagation process.
• Figure 3: Key observations.
• Figure 4: Confusion matrix
• Figure 5: Impact of LoS and user diversity on WiCross accuracy.