A Survey on State-of-the-Art Drowsiness Detection Techniques
Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8704263/ |
| _version_ | 1819170225453531136 |
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| author | Muhammad Ramzan Hikmat Ullah Khan Shahid Mahmood Awan Amina Ismail Mahwish Ilyas Ahsan Mahmood |
| author_facet | Muhammad Ramzan Hikmat Ullah Khan Shahid Mahmood Awan Amina Ismail Mahwish Ilyas Ahsan Mahmood |
| author_sort | Muhammad Ramzan |
| collection | DOAJ |
| description | Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field. |
| first_indexed | 2024-12-22T19:32:01Z |
| format | Article |
| id | doaj.art-f707fb96acc544aaab41e6664a30718e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:32:01Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f707fb96acc544aaab41e6664a30718e2022-12-21T18:15:05ZengIEEEIEEE Access2169-35362019-01-017619046191910.1109/ACCESS.2019.29143738704263A Survey on State-of-the-Art Drowsiness Detection TechniquesMuhammad Ramzan0Hikmat Ullah Khan1https://orcid.org/0000-0002-8178-6652Shahid Mahmood Awan2Amina Ismail3Mahwish Ilyas4Ahsan Mahmood5School of Systems and Technology, University of Management and Technology, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad at Wah Campus, Wah Cantt, PakistanSchool of Systems and Technology, University of Management and Technology, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad at Wah Campus, Wah Cantt, PakistanDepartment of Computer Science and Information Technology, University of Sargodha, Sargodha, PakistanDepartment of Computer Science, COMSATS University Islamabad at Attock Campus, Attock, PakistanDrowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.https://ieeexplore.ieee.org/document/8704263/Digital image processingdriver drowsinesssensorsfatigue detectionsupervised learningclassification |
| spellingShingle | Muhammad Ramzan Hikmat Ullah Khan Shahid Mahmood Awan Amina Ismail Mahwish Ilyas Ahsan Mahmood A Survey on State-of-the-Art Drowsiness Detection Techniques IEEE Access Digital image processing driver drowsiness sensors fatigue detection supervised learning classification |
| title | A Survey on State-of-the-Art Drowsiness Detection Techniques |
| title_full | A Survey on State-of-the-Art Drowsiness Detection Techniques |
| title_fullStr | A Survey on State-of-the-Art Drowsiness Detection Techniques |
| title_full_unstemmed | A Survey on State-of-the-Art Drowsiness Detection Techniques |
| title_short | A Survey on State-of-the-Art Drowsiness Detection Techniques |
| title_sort | survey on state of the art drowsiness detection techniques |
| topic | Digital image processing driver drowsiness sensors fatigue detection supervised learning classification |
| url | https://ieeexplore.ieee.org/document/8704263/ |
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