The Benefit of Distraction: Denoising Remote Vitals Measurements using
Inverse Attention
- URL: http://arxiv.org/abs/2010.07770v1
- Date: Wed, 14 Oct 2020 13:51:33 GMT
- Title: The Benefit of Distraction: Denoising Remote Vitals Measurements using
Inverse Attention
- Authors: Ewa Nowara, Daniel McDuff, Ashok Veeraraghavan
- Abstract summary: We present an approach that exploits the idea that statistics of noise may be shared between the regions that contain the signal of interest.
Our technique uses the inverse of an attention mask to generate a noise estimate that is then used to denoise temporal observations.
We show that this approach produces state-of-the-art results, increasing the signal-to-noise ratio by up to 5.8 dB.
- Score: 25.285955440420594
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Attention is a powerful concept in computer vision. End-to-end networks that
learn to focus selectively on regions of an image or video often perform
strongly. However, other image regions, while not necessarily containing the
signal of interest, may contain useful context. We present an approach that
exploits the idea that statistics of noise may be shared between the regions
that contain the signal of interest and those that do not. Our technique uses
the inverse of an attention mask to generate a noise estimate that is then used
to denoise temporal observations. We apply this to the task of camera-based
physiological measurement. A convolutional attention network is used to learn
which regions of a video contain the physiological signal and generate a
preliminary estimate. A noise estimate is obtained by using the pixel
intensities in the inverse regions of the learned attention mask, this in turn
is used to refine the estimate of the physiological signal. We perform
experiments on two large benchmark datasets and show that this approach
produces state-of-the-art results, increasing the signal-to-noise ratio by up
to 5.8 dB, reducing heart rate and breathing rate estimation error by as much
as 30%, recovering subtle pulse waveform dynamics, and generalizing from RGB to
NIR videos without retraining.
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