Real-time intelligent alarm system of driver fatigue based on video sequences

Developing intelligent systems to prevent car accidents can be very effective in minimizing accident death toll. One of the factors which play an important role in accidents is the human errors including driving fatigue relying on new smart techniques; this paper detects the signs of fatigue and sleepiness in the face of the person at the time of driving. The proposed system is based on three separate algorithms. In this model, the person's face is filmed by a camera in the first step by receiving 15fps video sequence. Then, the images are transformed from RGB space into YCbCr and HSV spaces. The face area is separated from other parts and highly accurate HDP is achieved. That the eyes are open or closed in a specific time interval is determined by focusing on thresholding and equations concerning the symmetry of human faces a finally using K-means Clustering, the frequency of yawning is identified. The proposed system has been implemented on four different video sequences with average accuracy of 93.18% and detection rate (DR) of 92.71% out of total 35000 image frames. High accuracy in segmentation, low error rate and quick processing of input data distinguishes this system from similar ones. This system can minimize the number of accidents caused by drivers' fatigue.