Respiration Rate Detection for In-Cabin Passenger Monitoring

Driver fatigue is a major factor in road accidents. To enhance road safety, this study proposes a novel deep learning model for detecting drivers' respiration rates using a thermal camera, an essential parameter for assessing drowsiness levels. Our approach predicts respiration rates directly without signal extraction from facial regions of interest, simplifying the detection process and potentially improving drowsiness detection systems. We evaluate and explored the model using capabilities on the new data acquired in a simulated driving environment which is divided in two subsets i.e. non-noisy and noisy datasets. Additionally, we introduce a unique data augmentation technique to reduce over-fitting in deep learning models utilizing temporal data. The implementation of this respiration detection model may contribute to driver drowsiness detection systems and enhance road safety.