通过角度优化与图信号去噪，提升视频测心率在运动中的准确性
Video-based Heart Rate Estimation with Angle-guided ROI Optimization and Graph Signal Denoising
- 基于面部角度动态优化感兴趣区域，捕捉全局运动
- 联合建模多区域信号，图信号处理使误差降低20.38%
- 可无缝集成现有方法，适合有运动干扰的远程心率监测场景
远程光体积描记法（rPPG）可通过面部视频实现非接触式心率测量，但说话、头部晃动等面部运动会显著降低其性能。为此，本文提出两个即插即用模块：角度引导的感兴趣区域自适应优化模块通过量化区域-相机夹角，优化受运动影响的信号并捕捉全局运动；多区域联合图信号去噪模块利用图信号处理，联合建模区域内与区域间信号，有效抑制运动伪影。该方法兼容基于反射模型的rPPG方法，在三个公开数据集上验证。结果表明，联合使用可显著降低平均绝对误差（MAE），较基线平均下降20.38%；消融实验也证实了各模块的有效性。研究展示了角度引导优化与图结构去噪在运动场景下提升rPPG性能的潜力。
原文摘要 · Abstract (English)
Remote photoplethysmography (rPPG) enables non-contact heart rate measurement from facial videos, but its performance is significantly degraded by facial motions such as speaking and head shaking. To address this issue, we propose two plug-and-play modules. The Angle-guided ROI Adaptive Optimization module quantifies ROI-Camera angles to refine motion-affected signals and capture global motion, while the Multi-region Joint Graph Signal Denoising module jointly models intra- and inter-regional ROI signals using graph signal processing to suppress motion artifacts. The modules are compatible with reflection model-based rPPG methods and validated on three public datasets. Results show that jointly use markedly reduces MAE, with an average decrease of 20.38\% over the baseline, while ablation studies confirm the effectiveness of each module. The work demonstrates the potential of angle-guided optimization and graph-based denoising to enhance rPPG performance in motion scenarios.
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