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Most smart systems such as smart home and smart health response to human'slocations and activities. However, traditional solutions are either requirewearable sensors or lead to leaking privacy. This work proposes an ambientradar solution which is a real-time, privacy secure and dark surroundingsresistant system. In this solution, we use a low power, Frequency-ModulatedContinuous Wave (FMCW) radar array to capture the reflected signals and thenconstruct to 3D image frames. This solution designs $1)$a data preprocessingmechanism to remove background static reflection, $2)$a signal processingmechanism to transfer received complex radar signals to a matrix containsspacial information, and $3)$ a Deep Learning scheme to filter broken framewhich caused by the rough surface of human's body. This solution has beenextensively evaluated in a research area and captures real-time human imagesthat are recognizable for specific activities. Our results show that the indoorcapturing is clear to be recognized frame by frame compares to camera recordedvideo.

Real Time 3D Indoor Human Image Capturing Based on FMCW Radar