Driver Fatigue Detection by Fusing Multiple Cues

Authors:
Rajinda Senaratne;David Hardy;Bill Vanderaa;Saman Halgamuge
Affiliations:
Dynamic Systems and Control Research Group, Department of Mechanical and Manufacturing Engineering, The University of Melbourne, Australia;Dynamic Systems and Control Research Group, Department of Mechanical and Manufacturing Engineering, The University of Melbourne, Australia;Dynamic Systems and Control Research Group, Department of Mechanical and Manufacturing Engineering, The University of Melbourne, Australia;Dynamic Systems and Control Research Group, Department of Mechanical and Manufacturing Engineering, The University of Melbourne, Australia
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

A video-based driver fatigue detection system is presented. The system automatically locates the face in the first frame, and then tracks the eyes in subsequent frames. Four cues which characterises fatigue are used to determine the fatigue level. We used Support Vector Machines to estimate the percentage eye closure, which is the strongest cue. Improved results were achieved by using Support Vector Machines in comparison to Naive Bayes classifier. The performance was further improved by fusing all four cues using fuzzy rules.