Abstract
Sürücü Yorgunluk tespit sistemleri, sürücüyü izleyerek, normal sürüş alışkanlığı dışında farklı tutum algılanması durumunda bunun yorgunluktan kaynaklandığını tespit ederek, sürücünün yolculuğa ara verilmesi gerektiğini uyarmaktadır. Bu sayede, sürücüye doğru karar vermesi konusunda destek sağlamaktadır. Bu çalışmada, sürücü yorgunluk tespiti ve uyarı sistemi ile sürücünün yorgunluğundan kaynaklanan trafik kazalarının uyarıcı bir sistemle engellenmesi amaçlanmıştır. Sistem, sürücünün göz hareketlerindeki değişimleri gerçek zamanlı olarak analiz etmekte ve gerektiğinde sürücüye uyarı vermektedir. Bu sayede daha güvenli bir sürüş sağlanacaktır.
Önerilen sistem sürücünün yorgunluğunu tespit etmek için çeşitli aşamalardan oluşmaktadır. Sürücünün yüz ve göz bölgelerinin tespit edilip her kare işlendikten sonra göz kırpma yüzdesi hesaplanarak sürücü yorgunluğu tespit edilmiştir. Sistemde gömülü sistem olarak Raspbian işletim sistemine sahip Raspberry Pi 4 kullanılmıştır ve OpenCV kütüphanesinden yararlanılmıştır.
Keywords
References
- T. D’Orazio, M. Leo, C. Guaragnella, A. Distante, A visual approach for driver inattention detection. Pattern Recog. 2007, 40, 2341–2355.
- M. Patel, S.K.L. Lal, D. Kavanagh, P. Rossiter, Applying Neural Network Analysis On Heart Rate Variability Data To Assess Driver Fatigue. Exp. Syst. Appl. 2011, 38, 7235–7242.
- L.M. Bergasa, J. Nuevo, M.A. Sotelo, R. Barea, M.E. Lopez, Real-Time System For Monitoring Driver Vigilance. IEEE Trans. Intell. Transport. Syst. 2006, 7, 63–77.
- Z. Zhang, J. Zhang, A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue. J. Contr. Theor. Appl. 2010, 8, 181–188.
- L. Pauly and D. Sankar, “Detection of drowsiness based on hog features and svm classifiers,” in 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 181–186, Nov 2015.
- A. Ercil, G. Littlewort, M. Bartlett, J. Movellan, Human Computer Interaction. Vol. 4796. Springer; Berlin, Germany: 2007. Drowsy driver detection through facial movement analysis; pp. 6–18.181–18.
- T. Danisman, I.M. Bilasco, C. Djeraba and N. Ihaddadene, 2010 “Drowsy driver detection system using eye blink patterns.” 2010 Int. Conf. Mach. Web Intell. ICMWI 2010 – Proc., pp. 230–233.
- M. Flores, J. Armingol, A. de la Escalera, Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions.EURASIP J. Adv. Signal Process.,2010, 438205.
- S. Mehta, S. Dadhich, S. Gumber and A.Jadhav Bhatt, “Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye ClosureRatio,”SSRN Electronic Journal, pp. 1333–1339, 2019.
- T. Soukupova and J. Cech. Real-Time Eye Blink Detection using Facial Landmarks. Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague. Prague,2016.
- J. W. Baek, B.-g. Han, K.-j. Kim, Y.-s. Chung, and S.-i. Lee, “Real-timeDrowsiness Detection Algorithm for Driver State Monitoring Systems,”2018 Tenth International Conference on Ubiquitous and Future Net-works (ICUFN), pp. 73–75, 2018.
Abstract
Driver Fatigue detection systems, by monitoring the driver, warns the driver that traveling should be interrupted by detecting that this is caused by fatigue if a different attitude other than normal driving habit is detected. In this way, it provides support to the driver in making the right decision. In this study, it was aimed to prevent traffic accidents caused by driver fatigue detection and warning system with a warning system. The system analyzes the changes in the eye movements of the driver in real time and warns the driver when necessary.
The proposed system consists of several steps to detect the driver's fatigue. Driver fatigue was determined by determining the face and eyes of the driver and calculating the blink percentage after each frame was processed. Raspberry Pi 4 with Raspbian operating system was used as an embedded system and OpenCV library was used.
Keywords
References
- T. D’Orazio, M. Leo, C. Guaragnella, A. Distante, A visual approach for driver inattention detection. Pattern Recog. 2007, 40, 2341–2355.
- M. Patel, S.K.L. Lal, D. Kavanagh, P. Rossiter, Applying Neural Network Analysis On Heart Rate Variability Data To Assess Driver Fatigue. Exp. Syst. Appl. 2011, 38, 7235–7242.
- L.M. Bergasa, J. Nuevo, M.A. Sotelo, R. Barea, M.E. Lopez, Real-Time System For Monitoring Driver Vigilance. IEEE Trans. Intell. Transport. Syst. 2006, 7, 63–77.
- Z. Zhang, J. Zhang, A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue. J. Contr. Theor. Appl. 2010, 8, 181–188.
- L. Pauly and D. Sankar, “Detection of drowsiness based on hog features and svm classifiers,” in 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 181–186, Nov 2015.
- A. Ercil, G. Littlewort, M. Bartlett, J. Movellan, Human Computer Interaction. Vol. 4796. Springer; Berlin, Germany: 2007. Drowsy driver detection through facial movement analysis; pp. 6–18.181–18.
- T. Danisman, I.M. Bilasco, C. Djeraba and N. Ihaddadene, 2010 “Drowsy driver detection system using eye blink patterns.” 2010 Int. Conf. Mach. Web Intell. ICMWI 2010 – Proc., pp. 230–233.
- M. Flores, J. Armingol, A. de la Escalera, Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions.EURASIP J. Adv. Signal Process.,2010, 438205.
- S. Mehta, S. Dadhich, S. Gumber and A.Jadhav Bhatt, “Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye ClosureRatio,”SSRN Electronic Journal, pp. 1333–1339, 2019.
- T. Soukupova and J. Cech. Real-Time Eye Blink Detection using Facial Landmarks. Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague. Prague,2016.
- J. W. Baek, B.-g. Han, K.-j. Kim, Y.-s. Chung, and S.-i. Lee, “Real-timeDrowsiness Detection Algorithm for Driver State Monitoring Systems,”2018 Tenth International Conference on Ubiquitous and Future Net-works (ICUFN), pp. 73–75, 2018.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | July 20, 2020 |
| Submission Date | June 8, 2020 |
| Acceptance Date | June 18, 2020 |
| Published in Issue | Year 2020 |
