Assistant driving systems have attracted more and more attention during recent years. Among them fatigue detection plays a key role because of its close relationship with accidents. In this paper, we propose a novel method which uses eye locating and tracking technique to detect driver fatigue. The present method consists of four steps. First, we employ Adaboost and Haar-like features to construct a robust classifier which can detect eye corner points. Second, we use extended parabolic Hough transformation to construct the parabola curves of upper and lower eyelid. Then, particle filter algorithm is used to track eye corner points in video sequences. Finally, the driver fatigue state is estimated through computing the frequency of eye opening and closing intervals. Experimental results from real environment datasets are given in our discussion as well.
Real-Time Eye Locating and Tracking for Driver Fatigue Detection
Applied Mechanics and Materials ; 20-23 ; 1359-1364
2010-01-12
6 pages
Article (Journal)
Electronic Resource
English
Real-time Driver Fatigue Detection System
Tema Archive | 2014
|Real-Time Driver Fatigue Detection Based on Eye State Recognition
Trans Tech Publications | 2013
|Real-Time Driver Fatigue Detection Based on Eye State Recognition
Tema Archive | 2013
|Eye Detection for a Real-Time Vehicle Driver Fatigue Monitoring System
British Library Conference Proceedings | 2011
|Real-Time Driver Fatigue Detection from ECG Using Deep Learning Algorithm
Springer Verlag | 2021
|