Abstract
Fatigue driving is one of main problems threatening driving safety. Therefore, it attracts numerous researchers interests. This paper introduces a new method based on eye feature to research the fatigue driving. Firstly, the face is detected by the model of skin-color in the YCbCr color space, which extracted face region from complex background quickly and accurately. Secondly, eye detection includes extracting eye region and detecting eye two steps. Specifically, the proposed method extracts eye region in face image based on gray-scale projection and then detect eye using Hough transform. Finally, calculate the area of the eye profile after dilation and use it as the parameter to analysis eye state. Put forward the standard to recognize fatigue base on the PERCLOS. The experiment results illustrate the efficiency and accurately of the proposed method, especially, detected face as well as extracted eye region with a high accuracy.
This is a preview of subscription content, log in via an institution.
References
Pei, Z., Song, Z.H., Zhou, Y.M.: Research status and development trend of motor vehicle driver fatigue evaluation method. J. China Agric. Univ. 6(6), 101–105 (2001)
Lampetch, S., Punsawad, Y., Wongsawat, Y.: EEG-based mental fatigue prediction for driving application. In: Biomedical Engineering International Conference (BMEICON), pp. 1–5 (2012)
Vicente, J., Laguna, P., Bartra, A., Bailon, R.: Detection of driver’s drowsiness by means of HRV analysis. In: Computing in Cardiology, pp. 89–92 (2011)
Wang, P., Shen, L.: A method detecting driver drowsiness state based on multi-features of face. In: 2012 5th International Congress on Image and Signal Processing (CISP 2012), pp. 1171–1175 (2012)
Lee, B.G., Chung, W.Y.: Driver alertness monitoring using fusion of facial features and bio-signals. IEEE Sens. J. 12(7), 2416–2422 (2012)
Watta, P., Gandhi, N., Lakshmanan, S.: An Eigenface approach for estimating driver pose. In: 2000 Proceedings Intelligent Transportation Systems, pp. 376–381. IEEE (2000)
Ni, Q.K., Guo, C., Yang, J.: Research of face image recognition based on probabilistic neural networks. In: 2012 24th Chinese Control and Decision Conference (CCDC), pp. 3885–3888 (2012)
Shan, D., Ward, R.K.: Improved face representation by nonuniform multilevel selection of gabor convolution features. IEEE Trans. Sys. Man Cybern. Part B Cybern. 39(6), 1408–1419 (2009)
Zhao, Y.L., Gao, Z., Wu, W.X.: The detection algorithm of locomotive driverss fatigue based on vision. In: 2010 3rd International Congress on Image and Signal Processing (CISP2010), pp. 2686–2690 (2010)
Devi, M.S., Choudhari, M.V., et al.: Driver drowsiness detection using skin color algorithm and circular hough transform. In: 2011 Fourth International Conference on Emerging Trends in Engineering and Technology, pp. 129–134 (2009)
Wu, C.D., Zhang, C.B.: Detecting and locating method of human face in driver fatigue surveillance. J. Shenyang Jianzhu Univ. Nat. Sci. 25(2), 386–389 (2009)
Lu, L., Yang, Y., Wang, L., Tang, B.: Eye location based on gray projection. In: 2009 Third International Symposium on Intelligent Information Technology Application, pp. 58–60 (2009)
Feng, J.Q., Liu, W.B., Yu, S.L.: Eyes location based on gray-level integration projection. Comput. Simul. 22(4), 75–76 (2005)
Yang, Q.F., Gui, W.H., et al.: Eye location novel algorithm for fatigue driver. Comput. Eng. Appl. 44(6), 20–24 (2008)
Qu, P.S., Dong, W.H.: Eye states recognition based on eyelid curvature and fuzzy logic. Comput. Eng. Sci. 29(8), 50–53 (2007)
Pan, X.D., Li, J.X.: Eye state-based fatigue drive monitoring approach. J. Tongji Univ. Nat. Sci. 39(2), 231–235 (2011)
Wang, Y., Hu, J.W.: A method for detection of driver eye fatigue state based on 3G video. Electron. Sci. Tech. 24(10), 84–85 (2011)
Acknowledgments
This work is supported by the Basic Research Program (Natural Science Foundation) of Jiangsu Province of China (No.BK20130209), the Fundamental Research Funds for the Central Universities (No.2013QNA24), the Project Funded by China Postdoctoral Science Foundation (No.2014M560460), the Project Funded by Jiangsu Postdoctoral Science Foundation (No.1302037C).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, X., Cui, X., Wang, G., Sun, T., Feng, H. (2015). A New Method for Driver Fatigue Detection Based on Eye State. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-25754-9_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25753-2
Online ISBN: 978-3-319-25754-9
eBook Packages: Computer ScienceComputer Science (R0)