Detection of driver sleepiness and warning the driver in real-time using image processing and machine learning techniques

• Autorzy: Umut I. , Aki O. , Uçar E. , Öztürk L.

Identyfikatory

DOI

10.12913/22998624/69149

• Języki publikacji: EN

Abstrakty

EN

The aim of this study is to design and implement a system that detect driver sleepiness and warn driver in real-time using image processing and machine learning techniques.Viola-Jones detector was used for segmenting face and eye images from the camera-acquired driver video. Left and right eye images were combined into a single image. Thus, an image was obtained in minimum dimensions containing both eyes. Features of these images were extracted by using Gabor filters. These features were used to classifying images for open and closed eyes. Five machine learning algorithms were evaluated with four volunteer’s eye image data set obtained from driving simulator. Nearest neighbor IBk algorithm has highest accuracy by 94.76% while J48 decision tree algorithm has fastest classification speed with 91.98% accuracy. J48 decision tree algorithm was recommended for real time running. PERCLOS the ratio of number of closed eyes in one minute period and CLOSDUR the duration of closed eyes were calculated. The driver is warned with the first level alarm when the PERCLOS value is 0.15 or above, and with second level alarm when it is 0.3 or above. In addition, when it is detected that the eyes remain closed for two seconds, the driver is also warned by the second level alarm regardless of the PERCLOS value. Designed and developed real-time application can able to detect driver sleepiness with 24 FPS image processing speed and 90% real time classification accuracy. Driver sleepiness were able to detect and driver was warned successfully in real time when sleepiness level of driver is achieved the defined threshold values.

Słowa kluczowe

EN

driver; sleepiness; real time; image processing; machine learning

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2017
• Tom: Vol. 11, no 2
• Strony: 95--102
• Opis fizyczny: Bibliogr. 23 poz., fig., tab.

Twórcy

autor

Umut I.

• Department of Computer Engineering, Faculty of Engineering, Trakya University, Ahmet Karadeniz Yerleşkesi, Edirne, Turkey

autor

Aki O.

• Department of Computer Engineering, Faculty of Engineering, Trakya University, Ahmet Karadeniz Yerleşkesi, Edirne, Turkey

autor

Uçar E.

• Department of Computer Engineering, Faculty of Engineering, Trakya University, Ahmet Karadeniz Yerleşkesi, Edirne, Turkey

autor

Öztürk L.

• Department of Physiology, Faculty of Medicine, Trakya University, Balkan Yerleşkesi, Edirne

Bibliografia

• 1. Association A.A. Asleep at the wheel: The prevalence and impact of drowsy driving. Professional Safety, 56(1), 2011, 12-12.
• 2. Bozkir M. G., Karakas P., and Oguz Ö. Vertical and horizontal neoclassical facial canons in turkish young adults. Surgical and Radiologic Anatomy, 26(3), 2004, 212-219.
• 3. Bradski G. and Kaehler A. Learning opencv: Computer vision with the opencv library. “ O’Reilly Media, Inc.”, 2008.
• 4. Chao W.L. Gabor wavelet transform and its application. R98942073 (TFA&WT final project), 2010.
• 5. Cyganek B. and Gruszczyński S. Hybrid computer vision system for drivers’ eye recognition and fatigue monitoring. Neurocomputing, 126, 2014, 78-94.
• 6. Dasgupta A., et al. An on-board vision based system for drowsiness detection in automotive drivers. International Journal of Advances in Engineering Sciences and Applied Mathematics, 5(2-3), 2013, 94-103.
• 7. Daza I. G., et al. Fusion of optimized indicators from advanced driver assistance systems (adas) for driver drowsiness detection. Sensors, 14(1), 2014, 1106-31.
• 8. Derpanis K.G. Gabor filters. York University, 2007.
• 9. Dhar S., et al. Implementation of real time visual attention monitoring algorithm of human drivers on an embedded platform. Proceedings of the 2010 IEEE Students’ Technology Symposium, 2010.
• 10. Fan X., et al. Gabor-based dynamic representation for human fatigue monitoring in facial image sequences. Pattern Recognition Letters, 31(3), 2010, 234-243.
• 11. Forsman P., et al. Feasibility of force platform based roadside drowsiness screening - a pilot study. Accident analysis and prevention, 62, 2014, 186-90.
• 12. Gang L. and Wan-Young C. Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier. Sensors, 13(12), 2013, 16494-16511.
• 13. Garces Correa A., Orosco L., and Laciar E. Automatic detection of drowsiness in eeg records based on multimodal analysis. Medical engineering & physics, 36(2), 2014, 244-9.
• 14. Gonçalves M., et al. Sleepiness at the wheel across europe: A survey of 19 countries. Journal of Sleep Research, 24(3), 2015, 242-253.
• 15. Hall M., et al. The weka data mining software: An update. ACM SIGKDD explorations newsletter, 11(1), 2009, 10-18.
• 16. Jimenez-Pinto J. and Torres-Torriti M. Face salient points and eyes tracking for robust drowsiness detection. Robotica, 30, 2012, 731-741.
• 17. Li X. P., Seignez E., and Loonis P. Driver drowsiness estimation by fusion of lane and eye features using a multilevel evidence theory. International Conference on Cyber Technology in Automation, Control & Intelligent Systems, 2013, 411.
• 18. Miguel T.T. and Javier J.P. Optical flow and driver’s kinematics analysis for state of alert sensing. Sensors, 13(4), 2013, 4225-4257.
• 19. Özer C., Etcibaşı Ş., and Öztürk L. Daytime sleepiness and sleep habits as risk factors of traffic accidents in a group of turkish public transport drivers. International journal of clinical and experimental medicine, 7(1), 2014, 268-73.
• 20. Öztürk L., Pelin Z., and Özer C. Sürücülerde epworth uykuluk skoru ile geçirilmiş ya da atlatılan trafik kazası sayısı arasındaki İlişki. 6. Ulusal Uyku ve Bozuklukları Kongresi, 2004.
• 21. Öztürk L., Tufan Y., and Güler F. Self-reported traffic accidents and sleepiness in a professional group of turkish drivers. Sleep and Hypnosis, 4(3), 2002, 106-110.
• 22. Selvakumar K., et al. Real-time vision based driver drowsiness detection using partial least squares analysis. Journal of Signal Processing Systems, 85(2), 2016, 263-274.
• 23. Vanderwerf F., et al. Eyelid movements: Behavioral studies of blinking in humans under different stimulus conditions. Journal of neurophysiology, 89(5), 2003, 2784-2796.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)