Abstract
Driver’s fatigue causes fatal road crashes and disrupts transportation systems. Specially in developing countries, drivers take more working hours and drive longer distances with short breaks to gain more money. This paper develops a new real time, low cost, and non-intrusive system that detects the features of the drivers’ fatigue. More specifically, the system detects fatigue from the eye closer and yawning. First, the face landmarks are extracted using the histogram of oriented gradients (HOG). Then, the support vector machine (SVM) model classifies the fatigue state from the non-fatigue. The accuracy of the SVM model presented by the area under the curve (AUC) is 95%. The system is evaluated with 10 participants in conditions that can affect the detection of the face. These conditions are different light conditions, gender, age groups, people wearing reading glasses, and males with beard and moustache around their mouth. The results are very promising, and it is 100% accurate.
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References
Yoassry, E.: Fatal and injury fatigue-related crashes on Ontario’s roads: a 5-year review why do we need an operational definition of fatigue-related crashes. In: Driver Fatigue Symposium 2007, vol. March, Canada, Toronto (2007). https://www.arrivealive.mobi/facts-about-drowsy-driving-internationally
Elshamly, A., Abd El-Hakim, F., Afify, H.: Factors affecting accidents risks among truck drivers in Egypt. In: MATEC Web of Conferences 2017, vol. January 94, Braşov, Romania (2017)
Car News Home Page. https://web.archive.org/web/20070927004541/http://www.newcarnet.co.uk/Lexus_news.html?id=5787. Accessed 5 Sept 2017
MR688 Homepage. http://www.care-drive.com/product/driver-fatigue-monitor-mr688. Accessed 25 Oct 2018
RVS-DC161-V4 Homepage. http://www.rvssystems.com/our-products/safety-accessories/professional-driver-fatigue-monitoring-system-rvs-dc161-v4.html. Accessed 25 Oct 2018
Sigari, M., Pourshahabi, M., Soryani, Fathy, M.: A review on driver face monitoring systems for fatigue and distraction detection. Int. J. Adv. Sci. Technol. 64, 73–100 (2014)
Saini, V., Saini, R.: Driver drowsiness detection system and techniques: a review. Int. J. Comput. Sci. Inf. Technol. 5(3), 4245–4424 (2014)
FORD Homepage. https://owner.ford.com/how-tos/vehicle-features/safety/driver-alert-system.html. Accessed 25 Oct 2018
VOLKSWAGEN Homepage. http://en.volkswagen.com/en/innovation-and-technology/technical-glossary/fahrerassistenzsysteme.html. Accessed 25 Oct 2018
BMW Homepage. https://www.bmw.ca/en/topics/experience/connected-drive/BMW%20ConnectedDrive:%20Driver%20Assistance%20.html. Accessed 25 Oct 2018
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition 2001, 8–14 Dec, pp. 511–518. IEEE Computer Society, Kauai, HI, USA (2001)
Turk, M., Pentland, A.: Face recognition using Eigen faces. In: Computer Vision and Pattern Recognition 1991, pp. 586–591. IEEE, Lahaina, Maui, Hawaii, USA (1991)
Wang, L., He, D.: Texture classification using texture spectrum. Pattern Recogn. 23(8), 905–910 (1990)
Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. 2(7), 1160–1169 (1985)
Dalal, N., Triggs, B.; Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition 2005, pp. 886–893. IEEE, San Diego, CA, USA, USA (2005)
Wierwille, W.: Historical perspective on slow eyelid closure: whence PERCLOS? In: Ocular Measures of Driver Alertness Conference 1999, MC-99-136, pp. 31–53. Federal Highway Administration Herndon, Washington, DC (1999)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Imaging Understanding Workshop, IJCAI, Canada, pp. 121–130 (1981)
King, D.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. Arch. 10, 1755–1758 (2009)
Acknowledgements
This work is partially supported by the ‘Science and Technology Development Fund’ (STDF) and the ‘German Egyptian Research Fund’ (GERF) Project Ref. No. 23059.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Additional informed consent was obtained from all individual participants for whom identifying information is included in this chapter.
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Nosseir, A., Hamad, A., Wahdan, A. (2020). Detecting Drivers’ Fatigue in Different Conditions Using Real-Time Non-intrusive System . In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1027. Springer, Singapore. https://doi.org/10.1007/978-981-32-9343-4_13
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DOI: https://doi.org/10.1007/978-981-32-9343-4_13
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