BENCHMARK OF PHYSIOLOGICAL MODEL BASED AND DEEP LEARNING BASED REMOTE PHOTOPLETHYSMOGRAPHY IN AUTOMOTIVE

Remote photoplethysmography (rPPG) can be used to monitor driver's cardio-respiratory functions in automotive for improving the safety of driving. To understand the challenges of rPPG in this application, we created a benchmark of latest rPPG technologies based on the MR-NIRP Car dataset, selecting the representative methods from both the physiological model based (PBV and DIS) and deep learning based (Supervised Learning and Contrastive Learning) approaches. The experimental results show that the physiological model based methods are generally more robust in this challenging setup with vigorous motions and dynamic lighting changes, typically DIS outperforms others, with an average MAE of 6.5~\,bpm on RGB videos and 15.9~\,bpm on NIR videos. The benchmark indicates that upgrading the single wavelength NIR setup to multi-wavelength is the essential step towards robust heart-rate monitoring in automotive.