Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi

Öz

Bu çalışmada, görüntü işleme tabanlı sürücü yorgunluk tespit sistemi ile yorgunluk ve uykusuzluğun yol açtığı trafik kazalarının önüne geçilmesi amaçlanmıştır. Geliştirilen sistem, farklı aydınlık seviyelerde sürücünün göz hareketlerini kameradan anlık olarak izlemekte, analiz etmekte ve gerekli durumda alarm vermektedir. Yorgunluk tespiti yapılırken PERCLOS (Percentage of Eye Closure) metriği kullanılmıştır. PERCLOS metriği tespit edilen gözlerin eşik değerler baz alınarak çevrilmiş binary görüntülerindeki piksel sayımı yapılıp ardından önceden hesaplanmış averaj değeri ile kıyaslanması sonucu gözlerin kapalı veya açık olduğuna karar verilmesi işlemlerine dayanmaktadır. Sürücüde yorgunluk tespiti yapıldığı anda Raspberry Pi 3 gömülü sistemi üzerinden alarm sisteminin devreye girmesi ve kablosuz haberleşme yardımı ile önceden belirlenmiş bir hesaba durum hakkında görüntülü ve yazılı bildirim yapılması sağlanmıştır. 

Anahtar Kelimeler

• Görüntü İşleme,Göz Tespiti,PERCLOS,Gömülü Sistem

Kaynakça

1. Liang, Y., Reyes, M.L., Lee, J.D. 2007. “Real-time detection of driver cognitive distraction using support vector machines”, IEEE Transact. Intell. Transport. Syst., 8, 340–350.
2. Chen, Y.L., Chiang, H. H., Chiang, C.Y., Liu, C.M., Yuan, S.M., Wang, J.H. 2012. “A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture”, Sensors, 12, 2373–2399.
3. Lee, B.G., Chung, W.Y.A. 2012. “Smartphone-based driver safety monitoring system using data fusion”, Sensors, 12, 17536–17552.
4. Lenskiy, A.A., Lee, J. 2012. “Driver’s eye blinking detection using novel color and texture segmentation algorithms”, International Journal of Control, Automation and Systems, 10, 317-327.
5. Liang, Y., Lee J.D. 2014. “A hybrid Bayesian Network approach to detect driver cognitive distraction”,Transport. Res. Part C: Emerg. Technol., 38, 146–155.
6. Masala, G.L., Grosso E. 2014. “Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses”, Transportation Research Part C, 49, 32-42.
7. Ghosh, S., Nandy, T., Manna, N. 2015. ”Real Time Eye Detection and Tracking Method for Driver Assistance System”, Advancements of Medical Electronics, Lecture Notes in Bioengineering, 13-25.
8. Cyganek, B. 2016. “Real-Time Eye Detection and Tracking in the Near-Infrared Video for Drivers’ Drowsiness Control”, Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, Advances in Intelligent Systems and Computing, 403, 481-490.

9. Takahashi, K. 2005. "Method of detecting concentration on cellular phone call from facial expression change by image processing", IEEE International Conference on Systems, Man and Cybernetics, 4, 3444-3448.
10. Zhang, X., Zheng, N., Wang, F., He. Y. 2011. “Visual Recognition of Driver Hand-held Cell Phone Use Based on Hidden CRF” IEEE International Conference on Vehicular Electronics and Safety (ICVES), 248–251.
11. Wang, D., Pei, M., Zhu, L. 2014. “Detecting Driver Use of Mobile Phone Based on In-Car Camera”, 10th International Conference on Computational Intelligence and Security, 148-151.
12. Ahmed,R., Emon, K.E.K., Hossain,M.F. .2014. “Robust Driver Fatigue Recognition Using Image Processing”, 3rd Int. IEEE Conference on Informatics, Electronics & Vision,1-6.
13. Beukman, A.R., Hancke, G.P., Silva, B.J. 2016, “A multi-sensor system for detection of driver fatigue”, Industrial Informatics IEEE 14th International Conference on (INDIN), South Africa
14. Fitriyani, N.L., Yang, C.K., Syafrudin, M. 2016. “Real-Time Eye State Detection System Using Haar Cascade Classifier and Circular Hough Transform”, IEEE 5th Global Conference on Consumer Electronics, Japan
15. Yan,J.J., Kuo,H.H., Lin,Y.F., Liao, T.L. 2016. “Real-time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing”, International Symposium on Computer, Consumer and Control (IS3C), China
16. Acıoğlu, A., Erçelebi, E. 2016. “Real Time Eye Detection Algorithm for PERCLOS Calculation”, 24th Signal Processing and Communication Application Conference (SIU), Turkey
17. Porwik, P., Lisowska, A. 2004. “The Haar–Wavelet Transform in Digital Image Processing: Its Status and Achievements”, Machine Graphics & Vision, 13, 79-98.
18. Viola, P., Jones, M. 2001. “Rapid Object Detection using a Boosted Cascade of Simple Features”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 511-518.
19. Dalal, N., Triggs B. 2005. “Histograms of oriented gradients for human detection”, Proceedings on IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 886-893.
20. Ahonen, T., Hadid, A., Pietikäinen, M. 2004. "Face Recognition with Local Binary Patterns," Proc. Eighth European Conf. Computer Vision, 469-481.
21. Ojala, T., Pietikäinen, M., Harwood, D. 1994, "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions", Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, 582 - 585.