Abstract: It is a means of injury assessment to detect respiratory rate using an unmanned aerial camera. However, the existing video-based respiratory rate detection algorithms are only applicable to fixed cameras. On the basis of spatial phase based respiratory signal extraction technology, a non-contact measurement method of human respiratory rate with videos recorded by unmanned aerial vehicle (UAV) is proposed. The complex steerable pyramid is used to extract the spatial phase of each frame image, and the phase sequence is obtained in chronological order. Secondly, the empirical mode decomposition (EMD) is used to decompose multiple modal components from the phase sequence, and the frequency variability analysis model is designed to select the components with stable frequencies, or the target respiratory signal. The peak value detection method is used to detect the human respiratory rate. The experimental results show that the average accuracy of the method can reach more than 98%, which is superior to the existing detection methods.

Key words: unmanned aerial vehicle, respiratory rate, empirical mode decomposition, frequency variability, spatial phase

摘要： 使用无人机载相机进行呼吸率检测是一种新兴的伤情评估手段，然而现有的视频呼吸率检测算法只适用于固定相机。在基于空间相位的呼吸信号提取技术基础上，提出一种基于无人机载视频的人体呼吸率非接触式测量方法，使用复可控金字塔提取出每一帧图像的空间相位，按时间顺序排列得到相位序列；接着采用经验模态分解从相位序列中拆解出多个模态分量，并设计频率变异性分析模型从中选择出具有稳定频率的分量，也即目标呼吸信号；利用峰值检测方法检测出人体呼吸率。实验结果表明，该方法在无人机晃动干扰下的呼吸率检测平均准确率能够达到98%以上，优于现有检测方法。

关键词: 无人机, 呼吸率, 经验模态分解, 频率变异性, 空间相位