提出抗光照变化的机器人摄像头心率估计方法，提升非接触生理感知鲁棒性。
Illumination-Robust Camera-Based Heart-Rate Estimation for Physiological Sensing in Robots
- 基于时空变换器框架，融合3D人脸对齐与光照增强机制。
- 在三种光照条件下实现0.79 bpm误差、0.982相关性，较基线提升93.6%。
- 适合需在复杂光照中稳定感知人类心率的交互机器人场景。
生理感知对服务、社交及辅助机器人在日常环境中的交互至关重要。远程光电容积脉搏波描记法（rPPG）可利用RGB摄像头实现非接触式心率（HR）估计，是机器人视觉系统理想的传感方式。然而，光照变化仍是其可靠部署的主要障碍。本文提出一种端到端的时空变换器框架，用于在新采集的多光照变化数据集上进行远程心率估计。该模型结合了基于PRNet的3D人脸对齐、片段级光照增强、残差时间标准化模块以及受控的混合时频监督策略。训练目标融合软偏移皮尔逊波形损失与谱KL散度损失，其中调节权重β控制频域心率引导的贡献。在覆盖三种光照水平的静态全级别混合测试协议中，β=5表现最优，达到最佳运行心率平均绝对误差（MAE）0.79 bpm，相关系数0.982。相较于在本数据集上评估的PhysFormer基线，本方法将心率MAE降低93.6%，同时将相关系数从0.088提升至0.982，证明其在光照变化下具备实际可用性。
原文摘要 · Abstract (English)
Physiological awareness is important for service, social, and assistive robots that interact with humans in everyday environments. Remote photoplethysmography (rPPG) enables non-contact heart-rate (HR) estimation from an RGB camera, making it a promising sensing modality for robot-mounted vision systems. However, illumination variation remains a major barrier to robust deployment. This paper presents an end-to-end spatial-temporal transformer framework for remote HR estimation on a new dataset with varied illumination. Our estimator integrates PRNet-based 3D face alignment, clip-level illumination augmentation, the Residual Temporal Standardization Module, and controlled hybrid temporal-frequency supervision. The training objective combines a Soft-Shifted Pearson waveform loss with a spectral Kullback-Leibler divergence loss, where a tuned weight ($\mathbfβ$) controls the contribution of frequency-domain heart-rate guidance. Experiments on a static all-level mix protocol covering three illumination levels show that $\mathbfβ=5$ provides the strongest result among the tested beta settings, achieving a best-run HR mean absolute error (MAE) of 0.79 bpm and an HR correlation of 0.982. Compared with the PhysFormer baseline evaluated on our dataset, our estimator reduces HR MAE by 93.6 %, while increasing HR correlation from 0.088 to 0.982, making it usable when illumination varies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。