A driver fatigue recognition model using fusion of multiple features

By using the fusion of contextual, visual and non-visual features, a model based on Dempster-Shafer (D-S) evidence theory is proposed to obtain a reliable driver's fatigue recognition. Firstly, an overall model structure is set up with respect to the selected features and key symptoms of driver...

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Bibliographic Details
Published in2005 IEEE International Conference on Systems, Man and Cybernetics Vol. 2; pp. 1777 - 1784 Vol. 2
Main Authors Yang, G., Lin, Y., Bhattacharya, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Biomedical monitoring
Computer crashes
contextual / physiological features
D-S evidence theory
Driver fatigue
Electrocardiography
Electromyography
Emotion recognition
Fatigue
Frequency
information fusion
recognition
Roads
Skin
US Department of Transportation
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Summary:By using the fusion of contextual, visual and non-visual features, a model based on Dempster-Shafer (D-S) evidence theory is proposed to obtain a reliable driver's fatigue recognition. Firstly, an overall model structure is set up with respect to the selected features and key symptoms of driver's fatigue. Secondly, a set of heuristic knowledge rules are used to determine the basic probability assignment; and a modified evidence combination is adopted to combine multiple pieces of evidence including consistent and conflicting ones. Thirdly, decision policy based on the basic probability assignment is applied to fatigue recognition. At last, an example is given to illustrate the proposed fatigue recognition model.
ISBN:9780780392984
0780392981
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2005.1571406