Title: Driver Fatigue Detection Analysis Based on Image Segmentation & Feature Extraction Using SVM


Volume 10 Issue 1 Year 2020

Authors:

S.R. Dogiwal

dogiwal@gmail.com
Department of Information Technology, Swami Keshvanand Institute of Technology, Management & Gramothan Jaipur-302017 (INDIA),

Vipin Sharma

ervipin85@gmail.com
Department of Information Technology, Swami Keshvanand Institute of Technology, Management & Gramothan Jaipur-302017 (INDIA)

Pages: 1-5


Abstract:

This paper presents a technique to implement real time image segmentation & drowsiness detection with the help of machine learning methodologies. Asa large number of individuals in world, lost their lives due to auto collisions. When all is said in done, the driver exhaustion alone records near by 25 % of the road mishaps and up to 60 % of road mishaps result in death or genuine damage. A fundamental driver of weariness is restlessness or a sleeping disorder. Therefore, a drivers' drowsiness state is a main consideration in serious street mishaps that claims a great many lives each year. In the ongoing years, utilization of wise calculations in autos has grown extensively. These structures use Wireless Sensor Networks to screen & transmit the state of the vehicle and driver. In the proposed research work Support Vector Machine based machine learning method has been implemented for image segmentation and emotion detection using facial expressions. The algorithm has been tested under various illuminance conditions and performed well with optimum visibility.

Keywords:
SVM, Image Processing, Drowsiness, Machine Learning, Segmentation, Emotion Detection