发表机构
Shandong University of Science and Technology; Southern University of Science and Technology; Nanjing University（山东科技大学; 南方科技大学; 南京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对车载高动态范围场景下rPPG因统一曝光导致信号损坏的问题，提出PixExpo框架，通过循环曝光和像素级融合选择最优观测，无需硬件改造，显著降低心率测量误差并提升成功率。
AI 中文摘要
远程光电容积描记（rPPG）为心率监测提供了一种有前景的非接触式解决方案，然而其在实际应用中的鲁棒性从根本上受到一个固有硬件限制的制约：现有的相机曝光控制范式，无论是固定曝光还是自动曝光，都在帧内的所有像素上施加统一的曝光时间。在诸如具有强烈定向阳光的汽车座舱等高动态范围场景中，这种空间不变的曝光约束不可避免地导致面部局部过曝或欠曝，在采集点不可逆地损坏了rPPG所必需的细微脉搏信号，这种物理退化是任何下游算法都无法恢复的。为克服这一瓶颈，我们提出了PixExpo（像素级曝光），一种“以时间换空间”的框架，它按照预定义的循环曝光计划顺序捕获帧，并执行非迭代的像素级融合。在每个像素位置，PixExpo选择最接近rPPG驱动目标强度的观测值。该标准旨在减少局部饱和和严重欠曝，而非优化感知外观。PixExpo无需传感器改造，但假设支持可编程的帧级曝光控制。我们使用新引入的MEX-Drive数据集验证了所提出的PixExpo框架，该数据集包含48名参与者在真实驾驶条件下的数据。实验结果表明，在具有挑战性的驾驶场景中，PixExpo优于制造商默认的自动曝光方法，将平均绝对误差（MAE）降低了7.21次/分钟（从13.94降至6.73次/分钟），并将成功率提高了37.29个百分点（从25.95%提升至63.24%）。
英文摘要
Remote photoplethysmography (rPPG) offers a promising non-contact solution for heart rate monitoring, yet its real-world robustness is fundamentally limited by an inherent hardware limitation: existing camera exposure control paradigms, whether fixed or auto-exposure, impose a uniform exposure time across all pixels within a frame. In high-dynamic-range scenes such as automotive cabins with strong directional sunlight, this spatially invariant exposure constraint inevitably leads to localized facial overexposure or underexposure, irreversibly corrupting the subtle pulsatile signals essential for rPPG at the point of capture, a physical degradation that no downstream algorithm can recover. To overcome this bottleneck, we propose PixExpo (Pixel-wise Exposure), a "temporal-for-spatial" framework that sequentially captures frames under a predefined cyclic exposure schedule and performs non-iterative pixel-wise fusion. At each pixel location, PixExpo selects the observation closest to an rPPG-motivated target intensity. This criterion seeks to reduce local saturation and severe underexposure rather than optimize perceptual appearance. PixExpo requires no sensor modification but assumes programmable frame-level exposure control. We validate the proposed PixExpo framework using our newly introduced MEX-Drive dataset, comprising 48 participants under real-world driving conditions. Experimental results demonstrate that PixExpo outperforms manufacture-default auto-exposure methods, reducing the mean absolute error (MAE) by 7.21 bpm (from 13.94 to 6.73 bpm) and increasing the success rate by 37.29 percentage points (from 25.95% to 63.24%) across challenging driving scenarios.
