A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection
This work addresses drowsiness detection for drivers and workers, but it is incremental as it builds on existing methods with a new dataset and baseline model.
The paper tackles early drowsiness detection by introducing a large public dataset of 60 subjects with video segments labeled for drowsiness levels and benchmarking a low-computational temporal model, achieving higher accuracy than human judgment.
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.