Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi
Driver’s 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...
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| Format: | Article |
| Language: | en |
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HRPub
2019
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| Online Access: | http://eprints.uthm.edu.my/689/1/DNJ9695_ae303c7c3c738e2a7d0a6afcce88a47b.pdf http://eprints.uthm.edu.my/689/ https://doi.org/10.13189/ujeee.2019.061609 |
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| _version_ | 1833416608474726400 |
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| author | Kamarudin, Nora Jumadi, Nur Anida |
| author_facet | Kamarudin, Nora Jumadi, Nur Anida |
| author_sort | Kamarudin, Nora |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Driver’s 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. |
| format | Article |
| id | my.uthm.eprints-689 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2019 |
| publisher | HRPub |
| record_format | eprints |
| spelling | my.uthm.eprints-6892021-10-17T05:02:49Z http://eprints.uthm.edu.my/689/ Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi Kamarudin, Nora Jumadi, Nur Anida QA76 Computer software Driver’s 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. HRPub 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/689/1/DNJ9695_ae303c7c3c738e2a7d0a6afcce88a47b.pdf Kamarudin, Nora and Jumadi, Nur Anida (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). pp. 67-75. https://doi.org/10.13189/ujeee.2019.061609 |
| spellingShingle | QA76 Computer software Kamarudin, Nora Jumadi, Nur Anida Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi |
| title | Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi |
| title_full | Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi |
| title_fullStr | Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi |
| title_full_unstemmed | Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi |
| title_short | Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi |
| title_sort | implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry pi |
| topic | QA76 Computer software |
| url | http://eprints.uthm.edu.my/689/1/DNJ9695_ae303c7c3c738e2a7d0a6afcce88a47b.pdf http://eprints.uthm.edu.my/689/ https://doi.org/10.13189/ujeee.2019.061609 |
| url_provider | http://eprints.uthm.edu.my/ |