Implementation of Driver Fatigue Monitoring System 


Vol. 37,  No. 8, pp. 711-720, Aug.  2012


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  Abstract

In this paper, we introduce the implementation of driver fatigue monitering system and its result. Input video device is selected commercially available web-cam camera. Haar transform is used to face detection and adopted illumination normalization is used for arbitrary illumination conditions. Facial image through illumination normalization is extracted using Haar face features easily. Eye candidate area through illumination normalization can be reduced by anthropometric measurement and eye detection is performed by PCA and Circle Mask mixture model. This methods achieve robust eye detection on arbitrary illumination changing conditions. Drowsiness state is determined by the level on illumination normalize eye images by a simple calculation. Our system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver’s doze level is detected. Our algorithm is implemented with low computation complexity and high recognition rate. We achieve 97% of correct detection rate through in-car environment experiments.

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  Cite this article

[IEEE Style]

J. Choi, H. Song, S. H. Park, C. Lee, "Implementation of Driver Fatigue Monitoring System," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 8, pp. 711-720, 2012. DOI: .

[ACM Style]

Jin-Mo Choi, Hyok Song, Sang Hyun Park, and Chul-Dong Lee. 2012. Implementation of Driver Fatigue Monitoring System. The Journal of Korean Institute of Communications and Information Sciences, 37, 8, (2012), 711-720. DOI: .

[KICS Style]

Jin-Mo Choi, Hyok Song, Sang Hyun Park, Chul-Dong Lee, "Implementation of Driver Fatigue Monitoring System," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 8, pp. 711-720, 8. 2012.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880