Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks

Published in Asian Conference on Computer Vision 2016, 2016

Recommended citation: Sanghyuk Park, Fei Pan, Sunghun Kang, Chang D. Yoo. Asian Conference on Computer Vision. Springer, Cham, 2016. ACCV 2016.



Statistics have shown that 20% of all road accidents are fatigue-related, and drowsy detection is a car safety algorithm that can alert a snoozing driver in hopes of preventing an accident. This paper proposes a deep architecture referred to as deep drowsiness detection (DDD) network for learning effective features and detecting drowsiness given a RGB input video of a driver. The DDD network consists of three deep networks for attaining global robustness to background and environmental variations and learning local facial movements and head gestures important for reliable detection. The outputs of the three networks are integrated and fed to a softmax classifier for drowsiness detection. Experimental results show that DDD achieves 73.06% detection accuracy on NTHU-drowsy driver detection benchmark dataset.