First Step Towards Creating a Software Package for Detecting the Dangerous States During Driver Eye Monitoring

摘要

The problem of detecting human fatigue by the state of the eyes is considered. A program for detecting the state of open/closed eyes has been developed. The Haar cascades were used to search for faces. Then the eyes were detected on the video from simple web-camera, which allowed us to accumulate a sufficient dataset. Training took place using convolutional neural networks, and due to different lighting conditions, different accuracy characteristics were obtained for the left and right eyes. Using Python programming language with the Jupyter Notebook functionality and the OpenCV library, a software package has been developed that allows us to highlight closed eyes when testing for a learning subject (certain person from whose images the model was trained) with an accuracy of about 90% on a camera with a low resolution (640 by 480 pixels). The proposed solution can be used in the tasks of monitoring driver's state because one of the most frequent reasons of road accidents is driver fatigue.