Respiratory rate is a vital sign indicating various health conditions. Traditional contact-based measurement methods are often uncomfortable, and alternatives like respiratory belts and smartwatches have limitations in cost and operability. Therefore, a non-contact method based on Pixel Intensity Changes (PIC) with RGB camera images is proposed. Experiments involved 3 sizes of bounding boxes, 3 filter options (Laplacian, Sobel, and no filter), and 2 corner detection algorithms (ShiTomasi and Harris), with tracking using the Lukas-Kanade algorithm. Eighteen configurations were tested on 67 subjects in static and dynamic conditions. The best results in static conditions were achieved with the Medium Bounding box, Sobel Filter, and Harris Method (MAE: 0.85, RMSE: 1.49). In dynamic conditions, the Large Bounding box with no filter and ShiTomasi, and Medium Bounding box with no filter and Harris, produced the lowest MAE (0.81) and RMSE (1.35)

翻译：呼吸频率是反映多种健康状况的重要生命体征。传统的接触式测量方法常令受试者感到不适，而呼吸带、智能手表等替代方案则在成本与操作性方面存在局限。为此，本文提出一种基于RGB摄像头图像像素强度变化的非接触式监测方法。实验涉及3种边界框尺寸、3种滤波器选项（拉普拉斯、索贝尔及无滤波器）以及2种角点检测算法（ShiTomasi与Harris），并采用Lukas-Kanade算法进行追踪。在静态与动态条件下，对67名受试者测试了18种配置组合。静态条件下的最佳结果由中等边界框结合索贝尔滤波器与Harris方法获得（平均绝对误差：0.85，均方根误差：1.49）。动态条件下，无滤波器结合ShiTomasi算法的大边界框配置，以及无滤波器结合Harris算法的中等边界框配置，取得了最低的平均绝对误差（0.81）与均方根误差（1.35）。