论文标题
对比度：通过时空对比度无监督的基于视频的远程生理测量
Contrast-Phys: Unsupervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast
论文作者
论文摘要
基于视频的远程生理测量利用面部视频来测量血量变化信号，这也称为远程光摄影学（RPPG）。 RPPG测量的监督方法可实现最先进的性能。但是，有监督的RPPG方法需要面部视频和地面真相生理信号才能进行模型培训。在本文中，我们提出了一种无监督的RPPG测量方法，该方法不需要地面真相信号进行培训。我们使用3DCNN模型在不同时空位置中从每个视频中生成多个RPPG信号，并以对比度损失训练该模型，其中将来自同一视频的RPPG信号汇总在一起，而来自不同视频中的那些视频则被推开。我们在五个公共数据集上测试，包括RGB视频和NIR视频。结果表明，我们的方法的表现优于先前的无监督基线，并且在所有五个数据集上的当前最佳监督RPPG方法非常接近准确性。此外，我们还证明了我们的方法可以以更快的速度运行，并且比以前的无监督基线更强大。我们的代码可从https://github.com/zhaodongsun/contrast-phys获得。
Video-based remote physiological measurement utilizes face videos to measure the blood volume change signal, which is also called remote photoplethysmography (rPPG). Supervised methods for rPPG measurements achieve state-of-the-art performance. However, supervised rPPG methods require face videos and ground truth physiological signals for model training. In this paper, we propose an unsupervised rPPG measurement method that does not require ground truth signals for training. We use a 3DCNN model to generate multiple rPPG signals from each video in different spatiotemporal locations and train the model with a contrastive loss where rPPG signals from the same video are pulled together while those from different videos are pushed away. We test on five public datasets, including RGB videos and NIR videos. The results show that our method outperforms the previous unsupervised baseline and achieves accuracies very close to the current best supervised rPPG methods on all five datasets. Furthermore, we also demonstrate that our approach can run at a much faster speed and is more robust to noises than the previous unsupervised baseline. Our code is available at https://github.com/zhaodongsun/contrast-phys.
