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.
Drowsiness is a major cause of accidents, in particular in road transportation. It is thus crucial to develop robust drowsiness monitoring systems. There is a widespread agreement that the best way to monitor drowsiness is by closely monitoring symptoms of drowsiness that are directly linked to the physiology of an operator such as a driver. The best systems are completely transparent to the operator until the moment he she must react. In transportation, cameras placed in the passenger compartment and looking at least at the face of the driver are most likely the best way to sense physiology related symptoms such as facial expressions and the fine behavior of the eyeballs and eyelids. We present here the new database1 called DROZY that provides multiple modalities of data to tackle the design of drowsiness monitoring systems and related experiments. We also present two novel systems developed using this database that can make predictions about the speed of reaction of an operator by using near-infrared intensity and range images of his her face.