Detection method of eyes opening and closing ratio for driver's fatigue monitoring
Abstract Eyes opening and closing status is one of the most important components to monitor the driver's fatigue.d The current research mainly considers eyes blink frequency and the closing duration to judge the driver's fatigue. To identify driver's fatigue level, eyes opening and cl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12002 |
| _version_ | 1797994980568989696 |
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| author | Qijie Zhao Junye Jiang Zhigao Lei Jingang Yi |
| author_facet | Qijie Zhao Junye Jiang Zhigao Lei Jingang Yi |
| author_sort | Qijie Zhao |
| collection | DOAJ |
| description | Abstract Eyes opening and closing status is one of the most important components to monitor the driver's fatigue.d The current research mainly considers eyes blink frequency and the closing duration to judge the driver's fatigue. To identify driver's fatigue level, eyes opening and closing ratio (EOCR) is a critical factor and therefore, it is desirable to detect EOCR for driver's fatigue monitoring. The proposed method aims to simultaneously segment images and measure the parameters of the EOCR. A BiSeNet‐based iris and pupil segmentation network is first proposed and the Visual Geometry Group (VGG) ConvNet‐based model to detect the EOCR value is provided by considering the main features rounding eyes area and the iris‐pupil size for building test dataset. The comparison experiments are conducted with the proposed method and the other existing work in different datasets, such as CASIA‐Iris‐Thousand, CASIA‐Iris‐Interval, and UBIRIS.v2. The results demonstrate that the proposed method has superior detection effects on both infrared images and colour images than other existing approaches. Furthermore, the experiments of detecting the EOCR and iris and pupil segmentation are carried out with the test dataset and the results show that the proposed method can reliably identify driver's eye opening and closing degree. |
| first_indexed | 2024-04-11T09:53:14Z |
| format | Article |
| id | doaj.art-9711e514d9f5435c9fdf77284eda0d1c |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-11T09:53:14Z |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-9711e514d9f5435c9fdf77284eda0d1c2022-12-22T04:30:43ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-01-01151314210.1049/itr2.12002Detection method of eyes opening and closing ratio for driver's fatigue monitoringQijie Zhao0Junye Jiang1Zhigao Lei2Jingang Yi3School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University Shanghai ChinaSchool of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University Shanghai ChinaSchool of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University Shanghai ChinaDepartment of Mechanical and Aerospace Engineering Rutgers University Piscataway New Jersey United States of AmericaAbstract Eyes opening and closing status is one of the most important components to monitor the driver's fatigue.d The current research mainly considers eyes blink frequency and the closing duration to judge the driver's fatigue. To identify driver's fatigue level, eyes opening and closing ratio (EOCR) is a critical factor and therefore, it is desirable to detect EOCR for driver's fatigue monitoring. The proposed method aims to simultaneously segment images and measure the parameters of the EOCR. A BiSeNet‐based iris and pupil segmentation network is first proposed and the Visual Geometry Group (VGG) ConvNet‐based model to detect the EOCR value is provided by considering the main features rounding eyes area and the iris‐pupil size for building test dataset. The comparison experiments are conducted with the proposed method and the other existing work in different datasets, such as CASIA‐Iris‐Thousand, CASIA‐Iris‐Interval, and UBIRIS.v2. The results demonstrate that the proposed method has superior detection effects on both infrared images and colour images than other existing approaches. Furthermore, the experiments of detecting the EOCR and iris and pupil segmentation are carried out with the test dataset and the results show that the proposed method can reliably identify driver's eye opening and closing degree.https://doi.org/10.1049/itr2.12002Optical, image and video signal processingImage recognitionComputer vision and image processing techniques |
| spellingShingle | Qijie Zhao Junye Jiang Zhigao Lei Jingang Yi Detection method of eyes opening and closing ratio for driver's fatigue monitoring IET Intelligent Transport Systems Optical, image and video signal processing Image recognition Computer vision and image processing techniques |
| title | Detection method of eyes opening and closing ratio for driver's fatigue monitoring |
| title_full | Detection method of eyes opening and closing ratio for driver's fatigue monitoring |
| title_fullStr | Detection method of eyes opening and closing ratio for driver's fatigue monitoring |
| title_full_unstemmed | Detection method of eyes opening and closing ratio for driver's fatigue monitoring |
| title_short | Detection method of eyes opening and closing ratio for driver's fatigue monitoring |
| title_sort | detection method of eyes opening and closing ratio for driver s fatigue monitoring |
| topic | Optical, image and video signal processing Image recognition Computer vision and image processing techniques |
| url | https://doi.org/10.1049/itr2.12002 |
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