Advertisement

Driver Fatigue Detection System Based on DSP Platform

  • Zibo Li
  • Fan Zhang
  • Guangmin Sun
  • Dequn Zhao
  • Kun Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9517)

Abstract

Due to non-invasiveness, monitoring driver state in computer vision (CV) has become a major way to detect driver fatigue. In contrast to other researches, we brought the driver fatigue detection system on the basis of DSP platform, which can make contribution to application on the integrated system for vehicle. However, the conventional system cannot easily be transplanted into DSP due to its storage and computation capacity. Therefore, designing an algorithm that can detect the fatigue efficiently is goal in this study. As the most important part of system, the geometric relationship and shape information within near frontal face is employed in the eye detection part, which depicts the eyebrow, eye and nose. In experiment part, a self-made database is assembled to test the performance of system. As the results of experiment, the detection rate of eye is achieved at 92.71 % and driver fatigue state is obtained at 97.5 %.

Keywords

Driver fatigue DSP platform Eye detection 

References

  1. 1.
    Ren, Y., Wang, S., Hou, B., et al.: A novel eye localization method with rotation invariance. IEEE Trans. Image Process. 23, 226–239 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wright, J., Yang, A.Y., Ganesh, A., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)CrossRefGoogle Scholar
  3. 3.
    Li, S.Z., Chu, R., Liao, S., et al.: Illumination invariant face recognition using near-infrared images. IEEE Trans. Pattern Anal. Mach. Intell. 29, 627–639 (2007)CrossRefGoogle Scholar
  4. 4.
    Song, L., Yu, F.: Driver fatigue detection system based on DSP. Comput. Eng. Des. 33, 519–522 (2012)MathSciNetGoogle Scholar
  5. 5.
    Everingham, M., Zisserman, A.: Regression and classification approaches to eye localization in face images. In: 7th International Conference of Automatic Face Gesture Recognition, pp. 2105–2112. IEEE Press, Southampton (2006)Google Scholar
  6. 6.
    Geng, X., Zhou, Z.H., et al.: Eye location based on hybrid projection function. J. Softw. 14, 1394–1400 (2003)zbMATHGoogle Scholar
  7. 7.
    Zhu, B.L., Feng, J.J., et al.: Multi-position eye location based on complex background. Appl. Res. Comput. 29, 1977–1979 (2012)Google Scholar
  8. 8.
    Zheng, Y., Wang, Z.F.: Minimal neighborhood mean projection function and its application to eye location. J. Softw. 19, 2322–2328 (2008)CrossRefGoogle Scholar
  9. 9.
    Zhang, Z., Zhang, J.: A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue. J. Control Theor. Appl. 8, 181–188 (2010)CrossRefGoogle Scholar
  10. 10.
    Gercia, H., Salazar, A., Alvarez, D., Orozco, Á.: Driving fatigue detection using active shape models. In: Bebis, G., et al. (eds.) ISVC 2010, Part III. LNCS, vol. 6455, pp. 171–180. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Söylemez, Ö.F., Ergen, B.: Eye location and eye state detection in facial images using circular Hough transform. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 141–147. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Zhang, W.Z., Wang, Z.C., et al.: Eye localization and state analysis for driver fatigue detection. J. Chongqing Univ. 36, 22–28 (2013)Google Scholar
  13. 13.
    Zhao, S., Yao, H., Sun, X.: Video classification and recommendation based on affective analysis of viewers. Neurocomputing 119, 101–110 (2013)CrossRefGoogle Scholar
  14. 14.
    Shen, F., Shen, C., Shi Q., et al.: Inductive hashing on manifolds. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1562–1569. IEEE Press, Portland (2013)Google Scholar
  15. 15.
    Yang, Y., Zha, Z., Gao, Y., et al.: Exploiting web images for semantic video indexing via robust sample-specific loss. IEEE Trans. Multimedia 16, 1677–1689 (2014)CrossRefGoogle Scholar
  16. 16.
    Yang, Y., Yang, Y., Huang, Z., et al.: Tag localization with spatial correlations and joint group sparsity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 881–888. IEEE Press, Providence (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zibo Li
    • 1
  • Fan Zhang
    • 1
  • Guangmin Sun
    • 1
    • 2
  • Dequn Zhao
    • 1
  • Kun Zheng
    • 1
  1. 1.Department of Electronic EngineeringBeijing University of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijingChina

Personalised recommendations