Drowsiness can put lives of many drivers and workers in danger. It is important to design practical and easy-todeploy 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 1 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 2 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.
Ghoddoosian et al. REF presented a large and public reallife dataset, which consists of 30 hours of video, with a range of content from subtle signs of drowsiness to more obvious ones.
A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection
ArXiv
ArXiv
Computer Science