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

## TL;DR

This paper presents a real-time, privacy-preserving 3D indoor human imaging system using FMCW radar, capable of capturing clear human images and activities without wearable sensors or privacy concerns.

## Contribution

It introduces a novel FMCW radar-based method with data preprocessing, signal processing, and deep learning filtering to achieve real-time 3D human imaging in indoor environments.

## Key findings

- Effective background static reflection removal
- High-quality 3D human image reconstruction
- Real-time recognition of human activities

## Abstract

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.