Real time Driver Drowisness Detection System

Project Title

Real time Driver Drowisness Detection System

Project Area of Specialization Artificial IntelligenceProject Summary

Drowsiness detection is a safety technology that can prevent accidents which are caused by drivers who fall asleep while driving. Driver inattention might be the result of a lack of alertness when driving due to driver drowsiness and distraction. Driver distraction occurs when an object or event
draws a person’s attention away from the driving task unlike driver distraction; driver drowsiness involves no triggering event but, instead, is characterized by a progressive withdrawal of attention from the road and traffic demands. Both driver drowsiness and distraction, however, might have the same effects i.e., decreased driving performance, longer reaction time, and an increased risk of crash involvement. The purpose of this is to build a drowsiness detection system that will detect that if a person’s eyes are closed for sufficient amount of time the system will alert the driver. The system
will generate an alarm when drowsiness is detected and the company will be notified by sms generated by system to ensure the safety of their driver.

Project Objectives

The main aim of the project is to save lives and our objective is to create better environment. Our proposed system will detect drowsiness of drivers to prevent accidents and improve safety on the roads and highways. It will also help transportation companies to assure the safety of their
passengers and goods.
? Save lives
? Prevent accidents
? Safer environment
? Ensure safety of goods

Project Implementation Method

Our project phases are defined broadly in three phases:
PHASE 1: Data Set Learning
The proposed system will be based on an already existing data set (haarcascade_eye.xml,
haarcascade_frontalface.xml) which is available online. This is basically a machine learning based approach where
a cascade function is trained from a lot of positive(i.e. images with faces) and negative images(i.e. images without
faces) and then used to detect objects. In this phase we will shortlist the best features relevant to the proposed
system and study these features in detail.
PHASE 2: Designing Base Structure
After completing the first phase, we need to extract the features by edge feature, line feature, four rectangle feature
and the base structure will be designed accordingly.
The real time detection will be implemented by algorithm which has following phases:
? Feature Selection (Haar feature selection)
? Adaboost training(selecting the best feature)
? Classifiers of Cascading (Cascading classifier)
? Implementing drowsiness detection using OpenCV, dlib and Python
PHASE 3: Final Deliverable
The proposed system in its final phase will have a fully functional face and eye detection and a working alarm
system with sms notification module and an interface to interact. The system will work in such a way that it will
derive input in real time from live stream and analyze the facial behavior to detect drowsiness of driver and to alert
the company through sms notification. 

Benefits of the Project

? The proposed system will provide a safer environment and help save countless lives.
? Will help in a secure delivery of goods transported by different companies.
? Warns the driver of drowsiness and the risk of sleep.
? Compliance with driver warnings helps to avoid crashes caused by fatigue.

Technical Details of Final Deliverable

The final deliverable of “Driver drowsiness system” will be an interactive user interface application along with documentation manual having all the technical details of system. It will be developed in Python language.
The application will be compatible with mini computers (i.e raspberry pi, arduino).

Final Deliverable of the Project Software SystemCore Industry ITOther Industries Transportation Core Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	61000
IP Camera	Equipment	1	8000	8000
Mini PC	Equipment	1	30000	30000
Cables and Connectors	Miscellaneous	1	2000	2000
SMS API	Miscellaneous	1	3000	3000
Respberry pi 4- 4 GB RAM	Equipment	1	18000	18000