Comparative analysis of various classification Machine Learning Algorithm on Driver Drowsiness Dataset

DR. UDAI BHAN TRIVEDI

Comparative analysis of various classification Machine Learning Algorithm on Driver Drowsiness Dataset

Authors: DR. UDAI BHAN TRIVEDI

Unique Paper ID: 155372
Volume: 8
Issue: 10
PageNo: 37-48

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 driverâ€™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.

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Cite This Article

ISSN: 2349-6002
Volume: 8
Issue: 10
PageNo: 37-48

Comparative analysis of various classification Machine Learning Algorithm on Driver Drowsiness Dataset

Available:https://ijirt.org/Article?manuscript=155372

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