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This paper introduces a novel exposure control framework, "Optimize-at-Capture," tailored for in-vehicle remote photoplethysmography (rPPG) to mitigate the impact of dynamic illumination changes on heart-rate monitoring. The framework proactively adjusts exposure parameters based on predictive modeling of skin reflections, optimizing for rPPG signal extraction rather than general image quality. Experiments on a new in-vehicle dataset (ExpDrive) demonstrate a significant reduction in heart-rate estimation error (6.31 bpm MAE reduction) and a substantial increase in success rate (32.3 percentage point increase) compared to fixed and standard auto-exposure methods.
Standard camera auto-exposure is blind to the needs of remote heart-rate monitoring, but a new method closes the gap to enable robust in-vehicle driver monitoring.
Remote photoplethysmography (rPPG) holds great promise for continuous heart-rate monitoring of drivers in intelligent vehicles. However, its performance is severely degraded by the highly dynamic illumination changes. A critical yet overlooked factor is the lack of exposure controlling during video acquisition -- most existing systems rely on either fixed exposure settings or camera build-in auto-exposure, both of which fail to maintain stable facial brightness under rapidly changing lighting conditions during driving. To address this gap, we propose a highly-adaptive exposure controlling framework that proactively adjusts exposure parameters based on predictive modeling of historical skin reflections. Unlike standard auto-exposure, our method is specifically optimized for rPPG measurement, ensuring the skin region of interest (ROI) remains within the optimal dynamic range for rPPG signal extraction. As an important contribution of this study, we introduce ExpDrive, a public in-vehicle physiological monitoring dataset comprising synchronized facial video and reference ECG from 48 subjects captured under real driving conditions. Extensive experiments demonstrate that our method consistently outperforms fixed exposure and standard auto-exposure strategies. Specifically, it reduces the Mean Absolute Error (MAE) by 6.31 bpm (from 14.1 to 7.79 bpm) and significantly increases the success rate by 32.3 percentage points (p<0.001) (from 24.9% to 57.2%) across challenging driving scenarios. Notably, it clearly improved the performance of non-contact heart-rate monitoring in both low-light (rainy) and high-glare (sunny) conditions, validating the efficacy of exposure-aware acquisition design.
