标题： 实习报告：基于深度学习的汽车领域成像PPG基准测试 显示英文标题

标题： Internship Report: Benchmark of Deep Learning-based Imaging PPG in Automotive Domain

摘要： 影像光电容积脉搏波图（iPPG）可用于驾驶过程中的心率监测，有望通过持续评估驾驶员的身体状况来减少交通事故。最近，基于深度学习的使用近红外（NIR）相机的iPPG方法引起了关注，被视为一种有前景的方法。为了帮助理解在汽车中应用iPPG所面临的挑战，我们通过在MR-NIRP Car数据集上评估其性能，提供了基于深度学习模型的NIR方法的一个基准。实验结果显示，在驾驶员头部保持静止或有小幅度运动的情况下，平均绝对误差（MAE）分别为7.5 bpm和16.6 bpm。这些发现表明，尽管该方法显示出潜力，但仍需要进一步改进以适应现实驾驶条件。 显示英文摘要

摘要： Imaging photoplethysmography (iPPG) can be used for heart rate monitoring during driving, which is expected to reduce traffic accidents by continuously assessing drivers' physical condition. Deep learning-based iPPG methods using near-infrared (NIR) cameras have recently gained attention as a promising approach. To help understand the challenges in applying iPPG in automotive, we provide a benchmark of a NIR-based method using a deep learning model by evaluating its performance on MR-NIRP Car dataset. Experiment results show that the average mean absolute error (MAE) is 7.5 bpm and 16.6 bpm under drivers' heads keeping still or having small motion, respectively. These findings suggest that while the method shows promise, further improvements are needed to make it reliable for real-world driving conditions.