Multi-feature analysis fatigue driving based on Conditional Local Neural Fields algorithm detection method
In order to effectively strengthen the monitoring technology of fatigue driving and reduce the incidence of traffic accidents, this paper proposes a new method based on conditional local neural fields algorithm for comprehensive evaluation of fatigue driving state. At present, most of the fatigue dr...
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| Vydáno v: | Journal of physics. Conference series Ročník 1237; číslo 2; s. 22157 - 22162 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Bristol
IOP Publishing
01.06.2019
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | In order to effectively strengthen the monitoring technology of fatigue driving and reduce the incidence of traffic accidents, this paper proposes a new method based on conditional local neural fields algorithm for comprehensive evaluation of fatigue driving state. At present, most of the fatigue driving detection algorithms are based on extracting a single characteristic index, which is strict in environmental requirements and not high in detection. In this paper, the HOG feature is combined with the CLNF algorithm to implement face detection and feature point localization. Then, the EPnP algorithm is used to estimate the head anomaly frequency based on the feature point information, and the blink frequency is calculated according to the EAR eye length aspect ratio concept according to the feature points around the eye. Finally the threshold set by the P80 fatigue detection standard in the PERCLOS method is integrated, and the distributed information fusion strategy is used for fatigue evaluation. Experimental results confirm the effectiveness of the method. |
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| AbstractList | In order to effectively strengthen the monitoring technology of fatigue driving and reduce the incidence of traffic accidents, this paper proposes a new method based on conditional local neural fields algorithm for comprehensive evaluation of fatigue driving state. At present, most of the fatigue driving detection algorithms are based on extracting a single characteristic index, which is strict in environmental requirements and not high in detection. In this paper, the HOG feature is combined with the CLNF algorithm to implement face detection and feature point localization. Then, the EPnP algorithm is used to estimate the head anomaly frequency based on the feature point information, and the blink frequency is calculated according to the EAR eye length aspect ratio concept according to the feature points around the eye. Finally the threshold set by the P80 fatigue detection standard in the PERCLOS method is integrated, and the distributed information fusion strategy is used for fatigue evaluation. Experimental results confirm the effectiveness of the method. |
| Author | Jiang, Jiangpeng Yao, Li Liu, Min Wan, Yan Zhao, Yingbin Fan, Jinghua |
| Author_xml | – sequence: 1 givenname: Yan surname: Wan fullname: Wan, Yan email: winniewan@dhu.edu.cn organization: School of Computer Science and Technology, Donghua University , China – sequence: 2 givenname: Min surname: Liu fullname: Liu, Min organization: School of Computer Science and Technology, Donghua University , China – sequence: 3 givenname: Jinghua surname: Fan fullname: Fan, Jinghua organization: School of Computer Science and Technology, Donghua University , China – sequence: 4 givenname: Yingbin surname: Zhao fullname: Zhao, Yingbin organization: School of Computer Science and Technology, Donghua University , China – sequence: 5 givenname: Jiangpeng surname: Jiang fullname: Jiang, Jiangpeng organization: School of Computer Science and Technology, Donghua University , China – sequence: 6 givenname: Li surname: Yao fullname: Yao, Li organization: School of Computer Science and Technology, Donghua University , China |
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| Cites_doi | 10.1007/s11263-008-0152-6 10.1016/S0736-0266(01)00079-1 |
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| SubjectTerms | Algorithms Aspect ratio Data integration Driver fatigue Face recognition Traffic accidents |
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