Design and Implementation of a Driver’s Eye State Recognition Algorithm Based on PERCLOS

PENG Yan; DONG Yan; CHENG Du

Article Contents

Article Navigation > Chinese Journal of Electronics > 2014 > 23(4): 669-672

PENG Yan, DONG Yan, CHENG Du, “Design and Implementation of a Driver’s Eye State Recognition Algorithm Based on PERCLOS,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 669-672, 2014,

Citation:

PENG Yan, DONG Yan, CHENG Du, “Design and Implementation of a Driver’s Eye State Recognition Algorithm Based on PERCLOS,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 669-672, 2014,

PENG Yan, DONG Yan, CHENG Du, “Design and Implementation of a Driver’s Eye State Recognition Algorithm Based on PERCLOS,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 669-672, 2014,

Citation:

PENG Yan, DONG Yan, CHENG Du, “Design and Implementation of a Driver’s Eye State Recognition Algorithm Based on PERCLOS,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 669-672, 2014,

PDF( 369 KB)

Design and Implementation of a Driver’s Eye State Recognition Algorithm Based on PERCLOS

1.
School of Management, Capital Normal University, Beijing 100089, China;
2.
Beijing Institute of Control Engineering, Beijing 100190, China;
3.
Beijing Kalends Inc, Beijing 100005, China

Funds: This work is supported by Beijing Natural Science Foundation (No.9142002).

Received Date: 2013-07-01
Rev Recd Date: 2013-08-01
Publish Date: 2014-10-05

Abstract

Abstract

In order to improve the general detection accuracy of eye state, this paper puts forward an innovative method for judging human eye state based on PERCLOS. After pretreatment of the eye image, Hough transformation is used for ellipse detection and pupil position. The gray projection variance threshold analysis is then used to help make the final detection. Freeman chain and the Snake model algorithm are used for the corner detection and precise calculation of the height of an open eye. Thus the PERCLOS value and the eye state can be figured out. The performance of our eye state recognition algorithm is validated by more than 1000 images within product database. The statistics result shows that the fatigue detection accuracy rate can meet the need of usage in complex environment.
- Eye state detection,
- Freeman chain code,
- Snake model,
- Hough circle,
- Ellipse detection

FullText(HTML)

References(8)

References

Mohammad Dehnavi and Mohammad Eshghi, Design and implementation of a real time and train less eye state recognition system Mohammad. EURASIP Journal on Advances in Signal Processing, pp.1-12, 2012.

Yingli Tian, Takeo Kanade and Jeffrey F. Cohn, Dual-state parametric eye tracking, Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp.110-115, 2000.

Ma Qiaomei, Xue Zhongjin and Zhu Linquan, Measuring method of eyes diopter based on image measuring technology, Chinese Journal of Electronics, Vol.19, No.3, pp.473-476, 2010.

Liang Jin-ming and Wei Zheng-xi, Research on improvement of the Ostu algorithm, Journal of Sichuan University of Science & Engineering (Natural Science Edition), Vol.23, No.5, pp.543-545, 2010. (in chinese)

Cheng Peiying, A novel OSTU segmentation algorithm for image threshold, Computer Applications and Software, Vol.26, No.5, pp.228-231, 2009.

Qiu Zhenyu and Wei Hui, Edge tracking algorithm and line segment detection based on freeman chain, Microcomputer Applications, Vol.24, No.1, pp.17-20, 2008

Li Ming, Wang Jiazhuo, Li Lingling and Li Cuihua, Curvature statistic corner detection, Mind and Computation, Vol.3, No.3, pp.209-214, 2009.

Huang Ying, Wang Zhiliang and Tu Xuyan, A real-time compensation strategy for non-contact gaze tracking under natural head movement, Chinese Journal of Electronics, Vol.19, No.3, pp.446-450, 2010.

Relative Articles

Supplements(0)

Cited By

Proportional views