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H. Kholerdi, N. TaheriNejad, R. Ghaderi, Y. Baleghi:
"Driver's Drowsiness Detection Using an Enhanced Image Processing Technique Inspired by the Human Visual System";
Connection Science, 28 (2016), 1; 27 - 46.

Unfit drivers are the cause of tens of thousands of incidents on the roads which lead to injuries and deaths. Therefore, it is very important to take preventive measures against such incidents. One of the unfit driving conditions is driving while being drowsy. Using image processing techniques, drowsiness of the driver could be detected and hence such incidents could be prevented. In this work, inspired by how images are processed by the human visual system, an enhancement for driver's drowsiness detection is suggested. Furthermore, to improve the robustness of the drowsiness detection system, the mechanism for using energy levels in frames is changed. Lastly, a better decision making process is proposed. To measure the merit of the system, it is applied to a set of drivers' data. Test results show that using the proposed system, success rate of the drowsiness detection system is 90%.

Fatigue and drowsiness detection, image processing, human visual system, sharpeningfilter, frame energy, decision tree

http://dx.doi.org/10.1080/09540091.2015.1130019

http://publik.tuwien.ac.at/files/publik_254954.pdf