论文标题
压力网：检测热视频中的压力
StressNet: Detecting Stress in Thermal Videos
论文作者
论文摘要
生理信号的精确测量对于有效监测人类生命体征至关重要。计算机视觉的最新发展表明，可以从人类的数字视频中提取诸如脉搏率和呼吸率之类的信号，从而增加了无接触式监控的可能性。本文提出了一种新的方法，用于从热视频中获得生理信号和分类应力状态。提出的网络 - “应力网”具有杂化发射表示模型，该模型模拟了皮肤和下面的血管对热量的直接发射和吸收。这会导致面部的信息丰富的特征表示，该特征由时空网络用于重建ISTI（初始收缩期时间间隔：心脏交感神经活动变化的量度，这被认为是人类压力的定量指数）。重建的ISTI信号被送入应力检测模型，以检测和分类个人的压力状态（即压力或无压力）。详细的评估表明，应力网络估计了ISTI信号的精度为95％，并以0.842的平均精度检测应力。源代码可在GitHub上找到。
Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed network--"StressNet"--features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal network for reconstructing the ISTI ( Initial Systolic Time Interval: a measure of change in cardiac sympathetic activity that is considered to be a quantitative index of stress in humans ). The reconstructed ISTI signal is fed into a stress-detection model to detect and classify the individual's stress state ( i.e. stress or no stress ). A detailed evaluation demonstrates that StressNet achieves estimated the ISTI signal with 95% accuracy and detect stress with average precision of 0.842. The source code is available on Github.
