Deep intelligent network for device-free people tracking: WIP abstract

Recent radio frequency (RF) sensing techniques use a network of RF sensors to detect and locate people that do not carry any devices and can operate in non line-of-sight environments. Model-based device-free RF sensing systems use statistical models to quantify human presence and motion based on the received RF signal measurements. However, such methods often require the fine tuning of multiple model-dependent parameters in order to achieve sub meter accuracy. In this work, we propose to use deep neural networks together with visual tracking systems to effectively generate training data so as to learn a general model. Our method can automatically produce human motion and occupancy images from RF sensor network measurements without the need for manual RF model parameter tuning.