Multispectral camera fusion increases robustness of ROI detection for biosignal estimation with nearables in real-world scenarios
Multispectral camera fusion increases robustness of ROI detection for biosignal estimation with nearables in real-world scenarios
复制标题
多光谱相机融合提高了 ROI 检测的稳健性，以在现实场景中使用邻近物体估计生物信号
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发表时间：
2018
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通讯作者：
Laura Tushaus
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作者：
Laura Tushaus
热像仪实现了对呼吸频率(RR)的非接触估计。RR的准确估计高度依赖于感兴趣区域(ROI)的可靠检测，特别是在使用低像素分辨率的相机时。提出了一种基于RGB摄像机人脸标志点检测的人脸感兴趣区域自动检测方法，该方法与跟踪后的热像融合。我们评估了新算法在具有挑战性的检测场景下对来自16个受试者的记录的检测率和空间准确性。结果表明，与人工标记相比，检测到的ROI中心具有很高的检测率(中位数：100%，第5-95%百分位数：92%-100%)和非常好的空间精度，平均均方根误差为2像素。因此，实现多光谱相机融合算法是提高具有热像仪的近距离设备非接触式RR估计可靠性的有效策略。
Thermal cameras enable non-contact estimation of the respiratory rate (RR). Accurate estimation of RR is highly dependent on the reliable detection of the region of interest (ROI), especially when using cameras with low pixel resolution. We present a novel approach for the automatic detection of the human nose ROI, based on facial landmark detection from an RGB camera that is fused with the thermal image after tracking. We evaluated the detection rate and spatial accuracy of the novel algorithm on recordings obtained from 16 subjects under challenging detection scenarios. Results show a high detection rate (median: 100%, 5th–95th percentile: 92%– 100%) and very good spatial accuracy with an average root mean square error of 2 pixels in the detected ROI center when compared to manual labeling. Therefore, the implementation of a multispectral camera fusion algorithm is a valid strategy to improve the reliability of non-contact RR estimation with nearable devices featuring thermal cameras.