Drowsiness detection system using eye aspect ratio technique
Transportation is widely used to allow user travel conveniently from place to place, for a personal of official purpose. Travel during peak hour or holiday, expose the driver to traffic jam for several hour, thus cause the drive to feel drowsy easily due to high concentration and lack of rest. This...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6213/1/P12435_2620b964aaacf2ca87e9e354da02cbcc.pdf |
| _version_ | 1825710149406294016 |
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| author | Sathasivam, Saravanaraj Mahamad, Abd Kadir Saon, Sharifah Sidek, Azmi Md Som, Mohamad Ameen, Hussein Ali |
| author_facet | Sathasivam, Saravanaraj Mahamad, Abd Kadir Saon, Sharifah Sidek, Azmi Md Som, Mohamad Ameen, Hussein Ali |
| author_sort | Sathasivam, Saravanaraj |
| collection | UTHM |
| description | Transportation is widely used to allow user travel conveniently from place to place, for a personal of official purpose. Travel during peak hour or holiday, expose the driver to traffic jam for several hour, thus cause the drive to feel drowsy easily due to high concentration and lack of rest. This situation contributes the increasing of the percentage of car incident due to car driver fatigue is the primary origin of the car accident. In this paper, image detection drowsiness system is proposed to detect the state of the car driver using Eye Aspect Ratio (EAR) technique. A developed system that occupied with the Pi camera, Raspberry Pi 4 and GPS module are used to detect and analyse continuously the state of eye closure in real time. This system able to recognize whether the driver is drowsy or not, with the initial, wearing spectacles, dim light and microsleep condition experimental conducted successfully give 90% of accuracy. This situation can increase the vigilant of drivers significantly. |
| first_indexed | 2024-03-05T21:53:14Z |
| format | Conference or Workshop Item |
| id | uthm.eprints-6213 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:53:14Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | uthm.eprints-62132022-01-31T06:52:52Z http://eprints.uthm.edu.my/6213/ Drowsiness detection system using eye aspect ratio technique Sathasivam, Saravanaraj Mahamad, Abd Kadir Saon, Sharifah Sidek, Azmi Md Som, Mohamad Ameen, Hussein Ali TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) Transportation is widely used to allow user travel conveniently from place to place, for a personal of official purpose. Travel during peak hour or holiday, expose the driver to traffic jam for several hour, thus cause the drive to feel drowsy easily due to high concentration and lack of rest. This situation contributes the increasing of the percentage of car incident due to car driver fatigue is the primary origin of the car accident. In this paper, image detection drowsiness system is proposed to detect the state of the car driver using Eye Aspect Ratio (EAR) technique. A developed system that occupied with the Pi camera, Raspberry Pi 4 and GPS module are used to detect and analyse continuously the state of eye closure in real time. This system able to recognize whether the driver is drowsy or not, with the initial, wearing spectacles, dim light and microsleep condition experimental conducted successfully give 90% of accuracy. This situation can increase the vigilant of drivers significantly. 2020 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/6213/1/P12435_2620b964aaacf2ca87e9e354da02cbcc.pdf Sathasivam, Saravanaraj and Mahamad, Abd Kadir and Saon, Sharifah and Sidek, Azmi and Md Som, Mohamad and Ameen, Hussein Ali (2020) Drowsiness detection system using eye aspect ratio technique. In: 2020 IEEE Student Conference on Research and Development (SCOReD), 27-28 September 2020, Johor, Malaysia. |
| spellingShingle | TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) Sathasivam, Saravanaraj Mahamad, Abd Kadir Saon, Sharifah Sidek, Azmi Md Som, Mohamad Ameen, Hussein Ali Drowsiness detection system using eye aspect ratio technique |
| title | Drowsiness detection system using eye aspect ratio technique |
| title_full | Drowsiness detection system using eye aspect ratio technique |
| title_fullStr | Drowsiness detection system using eye aspect ratio technique |
| title_full_unstemmed | Drowsiness detection system using eye aspect ratio technique |
| title_short | Drowsiness detection system using eye aspect ratio technique |
| title_sort | drowsiness detection system using eye aspect ratio technique |
| topic | TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) |
| url | http://eprints.uthm.edu.my/6213/1/P12435_2620b964aaacf2ca87e9e354da02cbcc.pdf |
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