Bio-Mechanical Distracted Driver Recognition Based on Stacked Autoencoder and Convolutional Neural Network

In this paper, we consider a problem of a biomechanical distraction of a driver; mostly it is related with hands secondary action while driving. Dealing with this type of distraction is crucial because the main causes of car accidents are due to negligent operation of in-vehicle technology, operating mobile phone while driving, and chatting with passenger. The effects of illumination conditions and driver's hands skin color make biomechanical distraction recognition very challenging. Accordingly, we propose a deep recognition model which can handle both effects, and its computational cost is relatively less when compared with the current state of the art. Our model is a sequential integration of two sub module: hand and face localizer and different types of biomechanical distraction recognizer. We conduct several simulations to investigate the effect of hand and face localization, and to measure the recognition performance of the proposed model, and its recognition performance, based on our experiment, our model achieves 98.68% validation accuracy which is better than the current state of the art.