Real-Time Drowsiness Identification based on Eye State Analysis

IEEE BASE PAPER ABSTRACT:

As per the previous year’s report concerning to road crashes indicates that the principal cause of such a fatal road accidents is because of negligence behavior as well as drowsiness of driver. This problem reveals the requirement of such a system that can recognize drowsiness state of driver and gives alert signal to the driver before the occurrence of any accidents. Therefore, this proposed work has established drowsy detection as well as accident avoidance system based on the eye blink duration. Here, first the open and close state of eye are detected based on the eye aspect ratio (EAR). Further, the blink duration or count during the changes of eye state from open to close are analyzed. Then, it identifies the state of drowsiness, when blink duration becomes more than a certain limits and sends the alert message to the driver through the alarm. Our developed system has shown the accuracy of 92.5 % approx on yawning dataset (YawDD).

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Open CV.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB.
Camera: Web Camera.

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Nageshwar Nath Pandey, Naresh Babu Muppalaneni, “Real-Time Drowsiness Identification based on Eye State Analysis”, IEEE Conference, 2021.

Python IEEE Projects

Real-Time Drowsiness Identification based on Eye State Analysis