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So, FPGA is one of the best platforms to implement a real time such as driver drowsiness system.
In this paper, a research project to develop a nonintrusive and autonomous driver drowsiness system based on Computer Vision and Artificial Intelligence has been presented.
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Eye state recognition is one of the main stages of many image processing systems such as driver drowsiness detection system and closed-eye photo correction.
The XF will put no one to sleep, but optional tech includes a driver drowsiness alert system.
In these circumstances, a fast and accurate driver drowsiness detection system can prevent these accidents.
The overall driver drowsiness detection system is shown in Figure 1.
PV ( j ) = ∑ i = 1 m IM ( i, j ), for j = 1 to PVlen (1) Figure 1 Block diagram of driver drowsiness detection system.
In a driver drowsiness detection system based on image processing, first the location of the face in the image is determined.
The paper by M. J. Flores et al., "Driver Drowsiness Warning System Using Visual Information for Both Diurnal and Nocturnal Illumination Conditions," seeks to locate, track, and analyze both the drivers face and eyes to compute a drowsiness index under varying light conditions (diurnal and nocturnal).
To analyze driver drowsiness several systems have been built in recent years.
This paper presents a system which detects driver drowsiness which works for both day and night time conditions and follows the classification presented in the state of the art.
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