Deep Learning-based rPPG Models towards Automotive Applications: A Benchmark Study

Tayssir Bouraffa, Dimitrios Koutsakis, Salvija Zelvyte; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1171-1180

Remote photoplethysmography (rPPG) has the potential to significantly enhance driver safety systems by enabling the detection of critical conditions such as driver drowsiness and sudden illness through non invasive monitoring of cardio-respiratory functions. However the dynamic environment within a vehicle characterized by motion artifacts and varying illumination presents unique challenges for accurate rPPG estimation. In this study we conducted a comprehensive benchmark of various supervised and unsupervised rPPG algorithms using the MR-NIRP car dataset to assess their performance in automotive settings. Qualitative and quantitative experiments were performed to evaluate and compare several rPPG models designed in stable noise-controlled environments highlighting the impact of real-world conditions on model performance. Our findings highlight the promise of machine learning approaches particularly neural network-based models in overcoming these challenges and accurately estimating heart and respiration rates in real-world driving scenarios. This study underscores the potential for integrating rPPG-based monitoring systems into vehicles to enhance driver safety and well-being.