Paper Title:
Eye Statement Recognition for Driver Fatigue Detection Based on Gabor Wavelet and HMM
  Abstract

Eye statement is one of the most important factors reflecting driver fatigue. A novel eye statement recognition method for driver fatigue detection based on Gabor transformation and Hidden Markov Model is proposed in this paper, in which, the eye detection algorithm is borrowed from Zafer Savas' TrackEye software, and Gabor features, i.e. the eye state features, of the eye are extracted by using Gabor wavelet. After that, by using these features, the classifier is trained by HMM (Hidden Markov Model) to distinguish the eye states including fatigue and alert, then the consecutive five frames are considered to judge whether there exists driver fatigue or not. Simulation results show that the new method has good accuracy and effectiveness.

  Info
Periodical
Chapter
Chapter 1: Mechanic Manufacturing System and Automation
Edited by
Zhixiang Hou
Pages
123-129
DOI
10.4028/www.scientific.net/AMM.128-129.123
Citation
H. Y. Yang, X. H. Jiang, L. Wang, Y. H. Zhang, "Eye Statement Recognition for Driver Fatigue Detection Based on Gabor Wavelet and HMM", Applied Mechanics and Materials, Vols. 128-129, pp. 123-129, 2012
Online since
October 2011
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Price
$32.00
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