Driver distraction detection using machine vision techniques

Abstract

This  article  presents  a  system  for  detecting  states  of  distraction  in  drivers  during  daylight  hours  using  machine  vision techniques, which is based on the image segmentation of the eyes and mouth of a person, with a front-face-view  camera.  From  said  segmentation  states  of  motion  of  the  mouth  and  head  are  established,  thus  allowing  to  infer the corresponding state of distraction. Images are extracted from short videos with a resolution of 640x480 pixels and image processing techniques such as color space transformation and histogram analysis are applied. A decision concerning the state of the driver is the result from a multilayer perceptron-type neural network with all extracted features as inputs. Achieved performance is 90% for a controlled environment screening test and 86% in real environment, with an average response time of 30 ms.