Driver Distraction Detection using Hybrid CNN Method

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Wijdan Abd Alhussain Abd Almutalib, et. al.


Traffic safety is an important issue in the world due to direct relationship with unsafe driving actions. This attitude belongs to the driver irresponsible behaviors. This research presents a solution for this problem by using modern technologies to follow the driver’s behavior. The present method applies two effective techniques, namely Gaussian Mixture Model GMM and YCbCr color, to extract the useful properties of the image and insert them in deep learning technique for the purpose of achieving the best way to monitor the driver’s movements. The proposed system consist of 3 stages: the first stage is preprocessing of RGB image to extract the region of interest (RoI). The second stage is segmentation process. it is achieved by converting the result of preprocessing stage to YCbCr color. The final stage is to use YCbCr outputs as an input representation to convolutional neural network (CNN) model to detect the final action. The main concern in this technique is to extract face and hands of the driver's body. These two parts represent the essential in the body that can be used to monitor the driver. The proposed model applied State Farm Dataset and achieved a classification accuracy of 96.59%. The results show that this method is superior in driver action recognition.

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