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Drowsiness Detection Based on Image Processing with Video Compression

Technologically advanced applications which prevent road accidents can minimize the accident death toll. This paper presents an optimized algorithm for minimizing false detection of drowsiness in drivers with more accuracy. Drowsiness detection in drivers at the early stages would help them to stay awake while driving. We have developed a system based on image processing to detect the signs of drowsiness. Driver is monitored using a camera which send live input for processing. Threshold equations concerning the symmetry of human faces are used in later part of processing. In this paper we have focused on detection of drowsiness using Viola-Jones face algorithm and Cross Correlation operation. If the findings crossed the predefined threshold level, warnings are generated. Alarm would be triggered if drowsiness is detected in drivers. Video compression using YCbCR enables higher throughput. Thus the proposed system is able to outperform the state-of-the-art drowsiness detection techniques. Furthermore, we managed to significantly increase the performance of the proposed system.