Comments (2)
This is only a simple reference implementation of rPPG. For more accuracy/more consistent HR estimates, try to have a consistent indoor light source, and as little as possible subject movement.
You are free to experiment with the minimum required relative face size: https://github.com/prouast/heartbeat/blob/master/RPPG.cpp#L23
For any of the other questions, please send me an email, thanks!
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@prouast I've had a similar problem. How does experimenting with minimum required relative face size improve rapid changes in BPM? If I understand correctly, REL_MIN_FACE_SIZE sets the minimum face size requirement in the haar cascade classifier to be 40% (default) of the frame size. Is this the correct understanding and if so, how does this help the problem? I've experimented with values from .01 to .99 and see no real difference. Thanks.
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Related Issues (20)
- How to run on windows10 HOT 3
- Increasing performance and tweaking HOT 3
- Does it work on Windows 10? HOT 3
- compile error on Debian9.8 with opencv & opencv_contrib 3.1.0 HOT 1
- Did you ever published an article about this work? HOT 1
- Sharing executable
- OSC output HOT 1
- get trigger at peak HOT 5
- use IR videos HOT 2
- Confusion on rescanning HOT 1
- getFps function and fps variable confusion HOT 1
- detrend algorithm? HOT 2
- How about only using keypoints and no masking? HOT 1
- The link in readme is broken HOT 1
- Does this repo work on windows too? HOT 1
- Measure high bpm after workout. HOT 2
- HEART
- Liveness Detection HOT 1
- Code performance and porting to Python HOT 1
- Poor performance with video files recorded using auto settings on DSLR camera HOT 1
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