Benchmark of Physiological Model Based and Deep Learning Based Remote Photoplethysmography in Automotive Applications

Zhiyu Wang1; Xuezhi Yang2; Hongzhou Lu3; Caifeng Shan4; Wenjin Wang1

10.1109/ICASSP49357.2023.10095078

2023

1520-6149

978-1-7281-6328-4

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2023-June

1-5

4-10 June 2023

Rhodes Island, Greece

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 algorithms 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 scenario 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.

Remote photoplethysmography driver monitoring physiological model deep learning

第一

EI

20234715105316

IEEE

1.Southern University of Science and Technology, China
2.Hefei University of Technology, China
3.The Third People’s Hospital of Shenzhen, China
4.Shandong University of Science and Technology, China

Zhiyu Wang,Xuezhi Yang,Hongzhou Lu,et al. Benchmark of Physiological Model Based and Deep Learning Based Remote Photoplethysmography in Automotive Applications[C],2023:1-5.