论文标题
ACTIVINET：基于计算机的基于计算机的方法来确定嗜睡
ActiveNet: A computer-vision based approach to determine lethargy
论文作者
论文摘要
Covid-19的爆发迫使每个人都留在室内，从而大幅度下降了身体活跃。我们的工作是基于制定骨干机制的想法，使用目标人的单眼图像实时检测活性水平。在许多应用程序，在线课程，安全监视等中，可以在许多应用下概括范围。我们提出了一种基于计算机视觉的多阶段方法，其中首先检测到一个人的姿势，用新颖的方法编码，然后通过经典的机器学习算法进行评估以确定活跃水平。通过向相关人员发送通知警报，围绕该方法包裹着警报系统，以提供抑制嗜睡的解决方案。
The outbreak of COVID-19 has forced everyone to stay indoors, fabricating a significant drop in physical activeness. Our work is constructed upon the idea to formulate a backbone mechanism, to detect levels of activeness in real-time, using a single monocular image of a target person. The scope can be generalized under many applications, be it in an interview, online classes, security surveillance, et cetera. We propose a Computer Vision based multi-stage approach, wherein the pose of a person is first detected, encoded with a novel approach, and then assessed by a classical machine learning algorithm to determine the level of activeness. An alerting system is wrapped around the approach to provide a solution to inhibit lethargy by sending notification alerts to individuals involved.
