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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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 1237; no. 2; pp. 22157 - 22162
Main Authors: Wan, Yan, Liu, Min, Fan, Jinghua, Zhao, Yingbin, Jiang, Jiangpeng, Yao, Li
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.06.2019
Subjects:
Algorithms
Aspect ratio
Data integration
Driver fatigue
Face recognition
Traffic accidents
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary: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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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1237/2/022157