Drowsy Driver Detection Using Viola-Jones Algorithm
Abstract
Drowsy driving is one of the factors that lead to road accidents which can cause dead. This is because driver does not able to give fully attention while driving. There are many factors that lead to driver drowsiness such as driving for a long time, do not have enough sleep and shift work. Thus, this research is proposed to develop a system to detect and alert drowsy driver by using Viola-Jones algorithm. Blinking rate is used as the indicator to determine either the driver is in drowsy or awake state. Viola-Jones algorithm is used to detect driver’s face and eyes in real time. Haar cascade classifier for frontal face and glasses eyes are used to train the system to detect driver’s face and eyes. In order to calculate eye blink, Eye Aspect Ratio (EAR) calculation is used to calculate and estimate of the eye-opening state in this system. The results of testing showed that the system with the Viola-Jones algorithm and Haar cascade classifier able to detect eyes blinking rate at the high accuracy percentages.
References
[2] S. Puja and C. Anurag, “Drowsy Driver Detection Using Image Processing,” vol. 9655, no. 6, pp. 135–143, 2017.
[3] V. Triyanti and H. Iridiastadi, “Challenges in detecting drowsiness based on driver’s behavior,” IOP Conf. Ser. Mater. Sci. Eng., vol. 277, no. 1, 2017, doi: 10.1088/1757-899X/277/1/012042.
[4] Zahra Mardi, Seyedeh Naghmeh Miri Ashtiani, and Mohamad Mikail, “EEG-based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests,” J Med Signals Sens, 2011. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3342623/.
[5] Ministry of Transport Malaysia, “National Transport Policy 2019-2030,” Minist. Transp., p. 101, 2019.
[6] R. Jabbar, K. Al-Khalifa, M. Kharbeche, W. Alhajyaseen, M. Jafari, and S. Jiang, “Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques,” Procedia Comput. Sci., vol. 130, pp. 400–407, 2018, doi: 10.1016/j.procs.2018.04.060.
[7] T. Vesselenyi, S. Moca, A. Rus, T. Mitran, and B. Tǎtaru, “Driver drowsiness detection using ANN image processing,” IOP Conf. Ser. Mater. Sci. Eng., vol. 252, no. 1, 2017, doi: 10.1088/1757-899X/252/1/012097.
[8] A. Islam, N. Rahaman, and M. Ahad, “A study on tiredness assessment by using eye blink detection,” J. Kejuruter., vol. 31, no. 2, pp. 209–214, 2019.
[9] İ. Umut, O. Aki, E. Uçar, and L. Öztürk, “Detection of Driver Sleepiness and Warning the Driver in Real-Time Using Image Processing and Machine Learning Techniques,” Adv. Sci. Technol. Res. J., vol. 11, no. 2, pp. 95–102, 2017, doi: 10.12913/22998624/69149.
[10] S. Z. Ouyang, L. Zhong, and R. Q. Luo, “The comparison and analysis of extracting video keyframe,” IOP Conf. Ser. Mater. Sci. Eng., vol. 359, no. 1, 2018, doi: 10.1088/1757-899X/359/1/012010.
[11] A. M. L. Katerine Diaz-Chito, Aura Hernández-Sabaté, A reduced feature set for driver head pose estimation. 2016.