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

A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection