DISTRACTED DRIVER DETECTION TECHNIQUES

The automobile industry is providing impetus to a rapidly growing economy in developed and developing countries. At the same time, the industry is emphasizing on adaptation of information technology within its periphery and beyond. Mandated by the federal and government regulations for safety, control and with ambitious plans in the pipeline, the automobile industry has reasons to be optimistic in incorporating disruptive technologies.

 

Distracted driving has been playing an active role in road crashes. Of late, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we address the problem of head pose estimation through a Convolutional Neural Network (CNN).

 

In this Paper an experiment is carried out to identify the distracted driver’s images, using a collective approach of Deep Convolution Neural Network(DCNN) and Computer Vision (C.V) in order to create a safe and accident free environment. The Computer Vision Techniques and principal component Analysis (PCA) are used to determine and extract the facial features. DCNN which is a leading-edge technology in visual recognition has been used to classify the images. These two state-of-the-art technologies have been brought together to obtain a more established classification of the images.