Visualization of Human Posture Based on Radar Time-Frequency Spectrogram
Yutao Xiang, Quan Tang, Guolong Cui, Shisheng Guo, Yong Qiang JIA, Chuan Chen · 2021 CIE International Conference on Radar (Radar) · 2021
Visualization of human postures is of great significance for research in areas such as autonomous driving, smart home monitoring and AR human interaction. However, RGB cameras and two-dimensional radar arrays commonly exploited for visualization of human postures have problems such as susceptibility to environmental interference and high cost. Based on this, the paper proposes a method to visualize human postures using radar time-frequency time-frequency spectrograms. A stepped-frequency continuous wave (SFCW) radar is needed to obtain radar time-frequency spectrograms in one direction, which realizes the visualization of human postures from the initial state of human and radar time-frequency time-frequency spectrograms. Experimental results demonstrate that this algorithm effectively solves the problem of using multiple radar information to visualize human postures.