Driver Fatigue Detection by Fusing Multiple Cues

  • Rajinda Senaratne
  • David Hardy
  • Bill Vanderaa
  • Saman Halgamuge
Conference paper

DOI: 10.1007/978-3-540-72393-6_96

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4492)
Cite this paper as:
Senaratne R., Hardy D., Vanderaa B., Halgamuge S. (2007) Driver Fatigue Detection by Fusing Multiple Cues. In: Liu D., Fei S., Hou Z., Zhang H., Sun C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Rajinda Senaratne
    • 1
  • David Hardy
    • 1
  • Bill Vanderaa
    • 1
  • Saman Halgamuge
    • 1
  1. 1.Dynamic Systems and Control Research Group, Department of Mechanical and Manufacturing Engineering, The University of MelbourneAustralia

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