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.
翻译:通过视频估算心率,可以使用病人护理、人际互动和体育方面的应用来进行非接触性健康监测; 现有的工作可以通过面部跟踪在某种运动下严格测量心率; 但是, 在不受限制的环境中, 这一点并不总是可能的, 因为脸部可能被隐蔽, 甚至是在镜头之外。 这里我们展示了 IntensePhysio: 具有挑战性的视频心率估计数据集, 包含现实的面部隔离、 严重主题运动和充足的心率变化。 为了在现实环境中确保心脏率差异, 我们记录每个对象的时间大约为1-2小时左右。 该主题正在使用一个带有摄像头的循环电动仪( 中度至高度) 进行( 中度 至 高度 ), 并且没有关于定位或移动的指示 。 我们有11个主题, 大约20 小时的视频。 我们展示了目前远程的光- 脉动摄影测量方法在估计这个环境中的心率方面有困难 。 此外, 我们介绍 IBIS- CNN, 一个使用磁脉动超像素的新基线,, 通过消除对视像头跟踪的需要, 将很快改进现有模型的代码和数据。