Abstract
With the improvement of sensor technology and significant algorithmic advances, the accuracy of remote heart rate monitoring technology has been significantly improved. Despite of the significant algorithmic advances, the performance of rPPG algorithm can degrade in the long-term, high-intensity continuous work occurred in evenings or insufficient light environments. One of the main challenges is that the lost facial details and low contrast cause the failure of detection and tracking. Also, insufficient lighting in video capturing hurts the quality of physiological signal. In this paper, we collect a large-scale dataset that was designed for remote heart rate estimation recorded with various illumination variations to evaluate the performance of the rPPG algorithm (Green, ICA, and POS). We also propose a low-light enhancement solution (technical solution) for remote heart rate estimation under the low-light condition. Using collected dataset, we found 1) face detection algorithm cannot detect faces in video captured in low light conditions; 2) A decrease in the amplitude of the pulsatile signal will lead to the noise signal to be in the dominant position; and 3) the chrominance-based method suffers from the limitation in the assumption about skin-tone will not hold, and Green and ICA method receive less influence than POS in dark illuminance environment. The proposed solution for rPPG process is effective to detect and improve the signal-to-noise ratio and precision of the pulsatile signal.
Abstract (translated)
随着传感器技术和算法的重大进展,远程心率监测技术的精度已经得到了显著提高。尽管出现了重大算法进展,但RPG算法的长期性能可能会下降,尤其是在傍晚或光线不足的环境下进行高强度连续工作。其中一个重要的挑战是,失去面部细节和低对比度会导致检测和跟踪失败。此外,视频捕获中的照明不足会影响生理信号的质量。在本文中,我们收集了一个大规模的数据集,该数据集设计用于远程心率估计,并采用各种照明变化来评估RPG算法的性能。我们还提出了在低光照条件下的远程心率估计技术的技术解决方案。通过收集的数据集,我们发现1) 面部检测算法在低光照条件下无法检测视频中的面部;2) 脉冲信号的振幅减少会导致噪声信号处于主导地位;3)基于颜色映射的方法受到肤色假设的限制,无法保持稳定,而Green和ICA方法在黑暗照明环境下的影响力比POS低。RPG算法提出的解决方案能够有效地检测和改善脉冲信号的信号到噪声比率和精度。
URL
https://arxiv.org/abs/2303.09336
