Train Driver Fatigue Detection Using Eye Feature Vector and Support Vector Machine

Abstract

Fatigue driving is one of the main causes of traffic accidents. The eye features are the important cues of fatigue detection. In order to improve the accuracy and robustness of detection based on a single eye feature, we propose a fatigue detection algorithm based on the eye feature (EFV) vector. Firstly, the coordinates of the eye region were localized with facial landmarks detector and the landmarks geometric relation (LGR) was calculated as a feature value. Secondly, a deep transfer learning network was designed to classify the driver eye state on a small dataset. The probability value of the eyes being open state was calculated. Then an eye feature vector was constructed to overcome the limitations of a single fixed threshold and a support vector machine (SVM) model was trained for eye state classification recognition. Finally, the performance of the proposed detection model was evaluated by the percentage of eyelid closure over time (PERCLOS) criterion. The results show that the accuracy of this model can reach 91.67% on the test database, which is higher than the single-feature-based method. This work lays a foundation for the online fatigue detection of train drivers and the deployment of the train driving monitoring system.