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 se parate 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 YC bC r and HS V 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.
In this paper, a new and effective method called HMAX is used for image texture. feature extraction. This method is inspired by the biological system of brain and human vision in order to create feature vectors for image recognition. A set of C2 features obtained from HMAX algorithm that are stable against changes in angle and size, are extracted from all image datasets firstly. Then using artificial neural networks and Knearest neighbor classifiers, eight different types of natural texture images from VISTEX dataset are classified. In order to evaluate the HMAX feature extraction method, the classification results are compared with Gabor filter banks. Since HMAX model is consistent with natural vision system, it is expected to obtain a better accuracy compared to Gabor filter banks. Experimental results with artificial neural network and K-nearest neighbor classifier show that the accuracy of 90.12% and 84.50% respectively for HMAX features. They have significant improvements compared to Gabor filter banks which obtained 78.62% and 72% accuracy.
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