Abstract
Driver fatigue is one of the leading causes of traffic accidents. At present, fatigue driving detection has disadvant ages such as low practical application effect and high equipment requirements. This paper proposes a multi-feature point non-invasive fatigue monitoring system based on a support vector machine with a hybrid kernel function. The system detects feature points through a gradient descent tree algorithm based on a cascaded regression and calculates the eye aspect ratio (EAR) and mouth aspect ratio (MAR). The heart rate is obtained through RGB image analysis combined with Euler’s video magnification algorithm. Classify facial features to get fatigued. This paper is based on the Logistic and Radial Basis Polynomial Kernel (RBPK) function to improve the support vector machine, which has better learning and generalization. Finally, this paper uses the Driver Drowsiness Detection Dataset and the author’s dataset to test. The classification accuracy rate for a single picture is 96.92%. In summary, the system proposed in this paper has a better recognition rate for fatigue driving detection.
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Acknowledgement
This work was supported by the Hainan Provincial Natural Science Foundation of China (Grant No. 2019RC041 and 2019RC098), Research and Application Project of Key Technologies for Blockchain Cross-chain Collaborative Monitoring and Traceability for Large-scale Distributed Denial of Service Attacks, National Natural Science Foundation of China (Grant No. 61762033), Opening Project of Shanghai Trusted Industrial Control Platform (Grant No. TICPSH202003005-ZC), and Education and Teaching Reform Research Project of Hainan University (Grant No. hdjy1970).
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Sun, Y. et al. (2021). Multi-dimensional Fatigue Driving Detection Method Based on SVM Improved by the Kernel Function. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-78618-2_3
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