Paper Info
Title
SparsePPG: Towards Driver Monitoring Using Camera-Based Vital Signs Estimation in Near-Infrared
Abstract
Camera-based measurement of the heartbeat signal from minute changes in the appearance of a person's skin is known as remote photoplethysmography (rPPG). Methods for rPPG have improved considerably in recent years, making possible its integration into applications such as telemedicine. Driver monitoring using in-car cameras is another potential application of this emerging technology. Unfortunately, there are several challenges unique to the driver monitoring context that must be overcome. First, there are drastic illumination changes on the driver's face, both during the day (as sun filters in and out of overhead trees, etc.) and at night (from streetlamps and oncoming headlights), which current rPPG algorithms cannot account for. We argue that these variations are significantly reduced by narrow-bandwidth near-infrared (NIR) active illumination at 940 nm, with matching bandpass filter on the camera. Second, the amount of motion during driving is significant. We perform a preliminary analysis of the motion magnitude and argue that any in-car solution must provide better robustness to motion artifacts. Third, low signal-to-noise ratio (SNR) and false peaks due to motion have the potential to confound the rPPG signal. To address these challenges, we develop a novel rPPG signal tracking and denoising algorithm (sparsePPG) based on Robust Principal Components Analysis and sparse frequency spectrum estimation. We release a new dataset of face videos collected simultaneously in RGB and NIR.We demonstrate that in each of these frequency ranges, our new method performs as well as or better than current state-of-the-art rPPG algorithms. Overall, our preliminary study indicates that while driver vital signs monitoring using cameras is promising, much work needs to be done in terms of improving robustness to motion artifacts before it becomes practical.
Year
DOI
Venue
2018
10.1109/CVPRW.2018.00174
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
Field
DocType
sun filters,overhead trees,oncoming headlights,preliminary analysis,motion magnitude,in-car solution,motion artifacts,false peaks,sparsePPG,Robust Principal Components Analysis,sparse frequency spectrum estimation,face videos,driver vital signs,camera-based measurement,heartbeat signal,remote photoplethysmography,in-car cameras,driver monitoring,camera-based vital sign estimation,rPPG signal tracking,person skin,narrow-bandwidth near-infrared active illumination,matching bandpass filter,signal-to-noise ratio,wavelength 940.0 nm
Computer vision,Heartbeat,Band-pass filter,Computer science,Near-infrared spectroscopy,Vital signs,Robustness (computer science),Frequency spectrum,RGB color model,Artificial intelligence,Principal component analysis
Conference
ISSN
ISBN
Citations 
2160-7508
978-1-5386-6101-7
1
PageRank 
References 
Authors
0.34
10
4
Name
Order
Citations
PageRank
Ewa Magdalena Nowara142.42
Tim K. Marks228119.41
Hassan Mansour334934.12
Ashok Veeraraghavan4149588.93