Cover More with Less: Eliminating Blind Spots for Surveillance Camera via Passive WiFi Sensing

Surveillance cameras, even with Pan, Tilt, and Zoom (PTZ) capabilities, can only cover a limited directional range at a time, leading to inevitable blind spots. To mitigate these blind spots, users typically need to deploy additional cameras or sensors (such as motion sensors and microphones) to guide the PTZ platform’s movement for capturing intruders, incurring additional deployment and maintenance costs. To address this challenge, this paper introduces a novel approach that utilizes the camera’s built-in WiFi module to detect potential intruders and direct the PTZ platform’s movements. The approach involves collecting WiFi Channel State Information (CSI) samples with humans positioned at various locations, and training a machine learning model to infer the real-time location of intruders. Given the intensive human effort required for collecting sample data, we developed an algorithm to optimize the selection of locations for collecting CSI samples. The algorithm assesses each location’s contribution to the overall success rate of capturing intruders, thereby achieving optimal sample distribution. Our evaluation demonstrates that our approach achieves a capture rate of $\mathbf{7 8. 2 4 \%}$, which is up to $24 \%$ higher than baseline methods, despite being trained with data collected from only $13 \%$ of the locations.