论文标题
心率估计在激烈的锻炼视频中
Heart rate estimation in intense exercise videos
论文作者
论文摘要
从视频中估算心率可以通过患者护理，人类互动和运动中的应用进行非接触健康监测。现有的工作可以通过面部跟踪在一定程度的运动下进行牢固测量心率。但是，在不受约束的设置中，这并不总是可以的，因为脸部可能会被遮住，甚至在相机外部。在这里，我们提出Intensephysio：具有挑战性的视频心率估计数据集，具有逼真的面部阻塞，严重的主题运动和充足的心率变化。为了确保在现实环境中的心率变化，我们将每个主题记录约1-2小时。该受试者正在用附有的摄像机进行循环测量计（以中度至高强度）锻炼，并且没有关于定位或移动的指示。我们有11个主题，大约有20个小时的视频。我们表明，在这种情况下，现有的远程光绘画方法在估计心率方面很难。此外，我们提出了IBIS-CNN，这是一种使用时空超级像素的新基线，通过消除对可见的面部/面部跟踪的需求，可以改善现有模型。我们将尽快使代码和数据公开可用。
Estimating heart rate from video allows non-contact health monitoring with applications in patient care, human interaction, and sports. Existing work can robustly measure heart rate under some degree of motion by face tracking. However, this is not always possible in unconstrained settings, as the face might be occluded or even outside the camera. Here, we present IntensePhysio: a challenging video heart rate estimation dataset with realistic face occlusions, severe subject motion, and ample heart rate variation. To ensure heart rate variation in a realistic setting we record each subject for around 1-2 hours. The subject is exercising (at a moderate to high intensity) on a cycling ergometer with an attached video camera and is given no instructions regarding positioning or movement. We have 11 subjects, and approximately 20 total hours of video. We show that the existing remote photo-plethysmography methods have difficulty in estimating heart rate in this setting. In addition, we present IBIS-CNN, a new baseline using spatio-temporal superpixels, which improves on existing models by eliminating the need for a visible face/face tracking. We will make the code and data publically available soon.
