A Method of Fatigue Driving State Detection Based on Deep Learning
Current domestic and overseas fatigue recognition algorithms are implemented using fatigue features which are mostly singular and man-made. Most of those algorithms have complex structure, low efficiency and weak adaptability in face of driver’s individual behavior habit. To this end, this paper put...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2018-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2018.06.400 |
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| Summary: | Current domestic and overseas fatigue recognition algorithms are implemented using fatigue features which are mostly singular and man-made. Most of those algorithms have complex structure, low efficiency and weak adaptability in face of driver’s individual behavior habit. To this end, this paper put forward a fatigue recognition algorithm based on deep learning. Firstly, the face image feature points are automatic extracted using convolutional neural network and landmark algorithm. Then the SVM algorithm is used to classify the fatigue characteristics. Finally, the fatigue state of the video stream image is detected based on the Perclos algorithm. The experimental results show that this method can obtain good fatigue characteristics, realize real-time fatigue detection, and its detection accuracy is 96.8%. |
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| ISSN: | 2096-5427 |