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Heart rate estimation in intense exercise videos
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.Comment: 4 pages, 4 figures, accepted at ICIP 202
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