论文标题
具有时间导数模块和时变不变损失的有效远程光绘画学
Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss
论文作者
论文摘要
我们提出了一种轻巧的神经模型，用于远程心率估计，重点是基于i）基于i）通过多种卷积衍生物的组合对PPG动力学进行有效的时空学习（PPG），以及II）的组合，并提高了模型的灵活性，以学习面部视频PPG和地面真相之间可能的外观。 PPG动力学是由通过多个卷积衍生物的增量聚合构建的时间导数模块（TDM）建模的，将Taylor系列扩展到所需顺序。对地面真理的稳健性是通过引入Talos（时间自适应位置变化）来处理的，这是对基于火车学习的模型的新时间损失。我们通过向公共纯和UBFC-RPPG数据集报告准确性和效率指标来验证我们的模型的有效性。与现有模型相比，我们的方法显示出竞争性的心率估计精度，参数数量较低，计算成本较低。
We present a lightweight neural model for remote heart rate estimation focused on the efficient spatio-temporal learning of facial photoplethysmography (PPG) based on i) modelling of PPG dynamics by combinations of multiple convolutional derivatives, and ii) increased flexibility of the model to learn possible offsets between the facial video PPG and the ground truth. PPG dynamics are modelled by a Temporal Derivative Module (TDM) constructed by the incremental aggregation of multiple convolutional derivatives, emulating a Taylor series expansion up to the desired order. Robustness to ground truth offsets is handled by the introduction of TALOS (Temporal Adaptive LOcation Shift), a new temporal loss to train learning-based models. We verify the effectiveness of our model by reporting accuracy and efficiency metrics on the public PURE and UBFC-rPPG datasets. Compared to existing models, our approach shows competitive heart rate estimation accuracy with a much lower number of parameters and lower computational cost.
