Comparative analysis of various classification Machine Learning Algorithm on Driver Drowsiness Dataset
Author(s):
DR. UDAI BHAN TRIVEDI
Keywords:
Classification, Machine Learning, Driver Drowsiness, EAR (Eye Aspect Ratio)
Abstract
Real Time Drowsiness behaviours of a driver related to fatigue are in the form of eye closing, head nodding. The focus of this research paper is on the detection of blinks of eye by estimating the EAR (Eye aspect Ratio). This is achieved by monitoring the eyes of the driver throughout the entire video sequence. An IR camera will be used for capturing live video of driver eyes in all light conditions and frames will extracted for image processing scheme of video capturing. The Various Binary classifying algorithm will be applied on Driver Drossiness Dataset and feature like sagging leaning of driver’s head and open/closed state of eyes will determine the state of the driver. The focus of this research paper is to classify the driver Drowsiness into two classes: 1. Open/Alert 2. Close/ Drowsiness through various classifying machine learning algorithms and determine the best performing algorithm for this purpose.
Article Details
Unique Paper ID: 155372

Publication Volume & Issue: Volume 8, Issue 10

Page(s): 37 - 48
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