Real Time 3D Indoor Human Image Capturing Based on FMCW Radar
This addresses privacy and environmental limitations in human monitoring for applications like smart homes and health, though it appears incremental as it builds on existing radar and deep learning techniques.
The paper tackles the problem of real-time 3D indoor human image capturing for smart systems by proposing an ambient FMCW radar solution that is privacy-secure and dark-resistant, achieving results where captured images are recognizable for specific activities and comparable to camera video.
Most smart systems such as smart home and smart health response to human's locations and activities. However, traditional solutions are either require wearable sensors or lead to leaking privacy. This work proposes an ambient radar solution which is a real-time, privacy secure and dark surroundings resistant system. In this solution, we use a low power, Frequency-Modulated Continuous Wave (FMCW) radar array to capture the reflected signals and then construct to 3D image frames. This solution designs $1)$a data preprocessing mechanism to remove background static reflection, $2)$a signal processing mechanism to transfer received complex radar signals to a matrix contains spacial information, and $3)$ a Deep Learning scheme to filter broken frame which caused by the rough surface of human's body. This solution has been extensively evaluated in a research area and captures real-time human images that are recognizable for specific activities. Our results show that the indoor capturing is clear to be recognized frame by frame compares to camera recorded video.