Journals Information
Universal Journal of Electrical and Electronic Engineering Vol. 6(5B), pp. 67 - 75
DOI: 10.13189/ujeee.2019.061609
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Implementation of Haar Cascade Classifier and Eye Aspect Ratio for Driver Drowsiness Detection Using Raspberry Pi
Nora Kamarudin 1, Nur Anida Jumadi 1,2,*, Ng Li Mun 1, Ng Chun Keat 1, Audrey Huong Kah Ching 1, Wan Mahani Hafizah Wan Mahmud 1, Marlia Morsin 1, Farhanahani Mahmud 1
1 Department of Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Malaysia
2 Institute of Integrated Engineering, Universiti Tun Hussein Onn Malaysia, Malaysia
ABSTRACT
Driver鈥檚 drowsiness is one of the leading contributing factors to the increasing accidents statistics in Malaysia. Therefore, the design and development of driver drowsiness detection based on image processing using Raspberry Pi camera module sensor interfacing with Raspberry Pi 3 board are proposed in this paper. To achieve the aim of the research, the Haar Cascade Classifier algorithm is implemented for eyes and face detection whereas for eyes blink (open and close) detection, the Eye Aspect Ratio (EAR) algorithm is employed. From several experiments conducted on six recruited subjects, the findings revealed that the accuracy of Haar Cascade classifier to detect the eyes and faces was subjected to correct sitting position (head must facing to the camera) as well as the eyes must not be covered with glasses or shades. Meanwhile, the range of average EAR value detected by the system was between 0.141 (eyes closed) and 0.339 (eyes opened). In conclusion, the image processing-based Haar Cascade and EAR algorithms utilized on Raspberry Pi platform have been successfully executed. For future improvement, the current board can be replaced with Raspberry Pi Touch Screen to minimize the hardware setup and the physiological based analysis using alcohol and heart rate sensors can be added.
KEYWORDS
Drowsiness, Eye Aspect Ratio, Haar Cascade Classifier, Raspberry Pi, Open CV, Python
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Nora Kamarudin , Nur Anida Jumadi , Ng Li Mun , Ng Chun Keat , Audrey Huong Kah Ching , Wan Mahani Hafizah Wan Mahmud , Marlia Morsin , Farhanahani Mahmud , "Implementation of Haar Cascade Classifier and Eye Aspect Ratio for Driver Drowsiness Detection Using Raspberry Pi," Universal Journal of Electrical and Electronic Engineering, Vol. 6, No. 5B, pp. 67 - 75, 2019. DOI: 10.13189/ujeee.2019.061609.
(b). APA Format:
Nora Kamarudin , Nur Anida Jumadi , Ng Li Mun , Ng Chun Keat , Audrey Huong Kah Ching , Wan Mahani Hafizah Wan Mahmud , Marlia Morsin , Farhanahani Mahmud (2019). Implementation of Haar Cascade Classifier and Eye Aspect Ratio for Driver Drowsiness Detection Using Raspberry Pi. Universal Journal of Electrical and Electronic Engineering, 6(5B), 67 - 75. DOI: 10.13189/ujeee.2019.061609.