Fatigued driving is a major cause of traffic accidents. Making accurate and effective fatigue driving detection has significant implications for road safety. This paper proposes a multi-feature fuzzy inference method for fatigue driving based on facial key points. Firstly, the supervised descent method is introduced into fatigue detection to get a more accurate location of facial key points. Secondly, according to the location of the two-dimensional facial key points, three-dimensional head model, and camera internal parameters, pitch angle that characterize the head pose is calculated iteratively. Finally, a multi-feature fuzzy inference method is adopted to judge the driver state based on the eye-blink, mouth-yawn, and head-tilt. Videos from the YawDD dataset and videos taken by ourselves are used to verify the algorithm of fatigue detection. The experimental results show that the average accuracy is 91.6%. The method is transplanted into the Samsung Exynos 4412 embedded development board. After some acceleration strategies applied, the system proposed in this paper meets the real-time requirements.
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