Drowsiness can put lives of many drivers and workers in danger. It is
important to design practical and easy-to-deploy real-world systems to detect
the onset of drowsiness.In this paper, we address early drowsiness detection,
which can provide early alerts and offer subjects ample time to react. We
present a large and public real-life dataset of 60 subjects, with video
segments labeled as alert, low vigilant, or drowsy. This dataset consists of
around 30 hours of video, with contents ranging from subtle signs of drowsiness
to more obvious ones. We also benchmark a temporal model for our dataset, which
has low computational and storage demands. The core of our proposed method is a
Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed
by detected blink features in sequence. Our experiments demonstrate the
relationship between the sequential blink features and drowsiness. In the
experimental results, our baseline method produces higher accuracy than human
judgment.

本文提出用于早期疲劳检测的多层次多尺度长短时记忆网络 (HM-LSTM)，结合测量到的眨眼特征，对大型真实数据集进行了测试，发现基于眼睑特征的检测方法比人类判断更准确。