Abstract
Fatigue is one of the important causes of car accidents. Analysis of falling asleep while driving helps to explain many of the most tragic events. To minimize the tragic consequences of falling asleep at the wheel, solutions were developed that allow early detection of fatigue symptoms. The article presents a system for the detection of symptoms of driver fatigue, based on an analysis of an image recorded by a camera. Two symptoms, that is slow blinking and yawning, are detected. To detect the symptoms of fatigue, cascade classifiers based on Local Binary Patterns were implemented. The classifiers were trained with the use of the OpenCV library. The system was tested on a collection of movies taken from the YawDD database. Conducted tests confirmed the correctness of the developed method. The impact of external factors, that could affect the effectiveness of the solution, was also analyzed. The system is able to correctly detect fatigue symptoms with an average accuracy of 99%. This result is comparable to the best published solutions.
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References
Łuczak, A., Zużewicz, K.: Zmęczenie kierowców a bezpieczeństwo pracy. Bezp. Pr. Nauka Prakt. nr 4, 20–23 (2006)
Osh in figures: Annex to Report: Occupational Safety and Health in the Road Transport sector: An Overview. European Agency for Safety and Health at Work (2011)
National Sleep Foundation. http://sleepfoundation.org/
Symon, E.: Wypadki drogowe w Polsce w 2017 roku. Wydział Opiniodawczo-Analityczny Biura Ruchu Drogowego Komendy Głównej Policji, Warszawa (2018)
Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., Hariri, B.: YawDD: a yawning detection dataset. In: Proceedings of the 5th ACM Multimedia Systems Conference, pp. 24–28. ACM, New York (2014)
Bradski, G., Kaehler, A.: The OpenCV Library. Dr Dobb’s J. Softw. Tools 25, 120–125 (2000)
Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45243-0_39
Shen, C., Wang, P., van den Hengel, A.: Optimally Training a Cascade Classifier. arXiv:1008.3742 Cs (2010)
The MPLab GENKI Database. http://mplab.ucsd.edu. Accessed 12 Dec 2018
Song, F., Tan, X., Liu, X., Chen, S.: Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recogn. 47, 2825–2838 (2014)
Youtube: megamayd: yawning. https://www.youtube.com/watch?v=o1sOuj3UOcs&t=1s. Accessed 12 Dec 2018
Youtube: AsapSCIENCE: The Yawn-O-Meter (How Long Can You Last?). https://www.youtube.com/watch?v=AJXX4vF6Zh0&t=2s. Accessed 12 Dec 2018
Youtube: BuzzFeedVideo: Can You Watch This Without Yawning? https://www.youtube.com/watch?v=M3QYDtSbhrA. Accessed 12 Dec 2018
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, p. I (2001)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24670-1_36
Im, J., Jeon, J., Hayes, M.H., Paik, J.: Single image-based ghost-free high dynamic range imaging using local histogram stretching and spatially-adaptive denoising. IEEE Trans. Consum. Electron. 57, 1478–1484 (2011)
Friedrichs, F., Yang, B.: Camera-based drowsiness reference for driver state classification under real driving conditions. In: 2010 IEEE Intelligent Vehicles Symposium, pp. 101–106 (2010)
Horng, W.-B., Chen, C.-Y., Chang, Y., Fan, C.-H.: Driver fatigue detection based on eye tracking and dynamic template matching. In: 2004 IEEE International Conference on Networking, Sensing and Control, pp. 7–12 (2004)
Fazli, S., Esfehani, P.: Tracking eye state for fatigue detection. Presented at the International Conference on Advances in Computer and Electrical Engineering (ICACEE 2012) (2012)
AL-Anizy, G.J., Nordin, M.J., Razooq, M.M.: Automatic driver drowsiness detection using haar algorithm and support vector machine techniques. Asian J. Appl. Sci. 8, 149–157 (2015)
Singh, A., Kaur, J.: Driver fatigue detection using machine vision approach. In: 2013 3rd IEEE International Advance Computing Conference (IACC), pp. 645–650 (2013)
Acknowledgements
This paper has been based on the results of a research project carried out within the framework of the fourth stage of the National Programme “Improvement of safety and working conditions” partly supported in 2017–2019 within the framework of research and development by the Ministry of Labour and Social Policy. The Central Institute for Labour Protection – National Research Institute is the Programme’s main coordinator.
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Majkowski, A., Kołodziej, M., Sawicki, D., Tarnowski, P., Rak, R.J., Kukiełka, A. (2019). Video Sequence Analysis Using Local Binary Patterns for the Detection of Driver Fatigue Symptoms. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_5
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