Driver yawning detection based on deep convolutional neural learning and robust nose tracking

Abstract

Driver yawning detection is one of the key technologies used in driver fatigue monitoring systems. Real-time driver yawning detection is a very challenging problem due to the dynamics in driver's movements and lighting conditions. In this paper, we present a yawning detection system that consists of a face detector, a nose detector, a nose tracker and a yawning detector. Deep learning algorithms are developed for detecting driver face area and nose location. A nose tracking algorithm that combines Kalman filter with a dedicated open-source TLD (Track-Learning-Detection) tracker is developed to generate robust tracking results under dynamic driving conditions. Finally a neural network is developed for yawning detection based on the features including nose tracking confidence value, gradient features around corners of mouth and face motion features. Experiments are conducted on real-world driving data, and results show that the deep convolutional networks can generate a satisfactory classification result for detecting driver's face and nose when compared with other pattern classification methods, and the proposed yawning detection system is effective in real-time detection of driver's yawning states.

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