Driving Detection Based on the Multifeature Fusion
In order to solve the problems of facial feature localization and driver fatigue state identification methods in driving fatigue detection, a driving detection method based on the multifeature fusion was proposed. This method uses a supervised descent algorithm to simultaneously locate multiple faci...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8266295 |
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| Summary: | In order to solve the problems of facial feature localization and driver fatigue state identification methods in driving fatigue detection, a driving detection method based on the multifeature fusion was proposed. This method uses a supervised descent algorithm to simultaneously locate multiple facial features of drivers. On the basis of blink, yawn and nod judgment, multiple characteristic values of blink frequency, yawn frequency, and nod frequency of drivers were extracted to establish a fatigue detection sample database, and a naive Bayes classifier was constructed to judge fatigue. When the driver appears fatigue driving, warning information is given in time in order to prevent traffic accidents. The experimental results show that two sample videos were selected for testing. The accuracy rate of video sample 1 and video sample 2 was 94.74% and 95.00%, respectively. Conclusion. In the actual driving environment video test results, the discriminant average accuracy of a driver fatigue state reaches 94.87%, which has a good performance. |
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| ISSN: | 1687-5257 |