Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network

Drowsy driver alert systems have been developed to minimize and prevent car accidents. Existing vision-based systems are usually restricted to using visual cues, depend on tedious parameter tuning, or cannot work under general conditions. One additional crucial issue is the lack of public datasets that can be used to evaluate the performance of different methods. In this paper, we introduce a novel hierarchical temporal Deep Belief Network (HTDBN) method for drowsy detection. Our scheme first extracts high-level facial and head feature representations and then use them to recognize drowsiness-related symptoms. Two continuous-hidden Markov models are constructed on top of the DBNs. These are used to model and capture the interactive relations between eyes, mouth and head motions. We also collect a large comprehensive dataset containing various ethnicities, genders, lighting conditions and driving scenarios in pursuit of wide variations of driver videos. Experimental results demonstrate the feasibility of the proposed HTDBN framework in detecting drowsiness based on different visual cues.