Drowsiness detection system using digital image process

Project Title

Drowsiness detection system using digital image process

Project Area of Specialization Wearables and ImplantableProject Summary

Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Every year, they increase the amounts of deaths and fatalities injuries globally. A module for Advanced Driver System (ADS) is presented to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety; this system deals with automatic driver drowsiness detection based on visual information. We propose an algorithm to locate, track, and analyze both the drivers face and eyes to measure a scientifically supported measure of drowsiness associated with slow eye closure.

Driver drowsiness is one of the critical causes of roadways accidents nowadays. Thus fatigue and drowsiness detection play vital role in preventing the corresponding road accidents. Over the last decade, many image processing based approaches were developed to detect driver's fatigue and drowsiness status. These approaches mainly focus on the extraction of the driver's face and predict the eye blinking rate from the eye region and the yawning rate. However, these features are not necessarily the best to describe the driver's fatigue level, because from one side, some drivers may have imbalanced eye blinking rate due to medical issues, and from the other side, some drivers may have high yawning rate while they have fully driving attention. An online face monitoring system was installed and a large list of eyes area features was extracted in spatial and frequency domain including two new features which are circularity and black ratio. Four support vectors machine classification models were developed based on combinations of the relevant features. The analysis of these models showed that the highest accuracy (91.3%) was achieved when the wavelet coefficients, texture features, circularity, and black ratio are employed. The results of the proposed approach indicated its promising inline implementation into car cabin to decide the driver's drowsiness status.

Project Objectives

Objectives:
To develop an embedded system that detects driver drowsiness level and warns him or her of his or her state.
Specific Objectives:
1. To be able to accurately detect eye from an image
2. To be able to detect the region of interest in this case the eyes
3. To accurately classify the state of the eye either closed or open
4. To provide a warning to the driver if drowsiness is detected.

Project Implementation Method

Block Diagram of the Project:

Benefits of the Project

The drowsiness detection and correction system developed is capable of detecting drowsiness in a rapid manner. The system which can differentiate normal eye blink and drowsiness which can prevent the driver from entering the state of sleepiness while driving. The system works well even in case of drivers wearing spectacles and under low light conditions also. During the monitoring, the system is able to decide if the eyes are opened or closed. When the eyes have been closed for about two seconds, the alarm beeps to alert the driver and the speed of the vehicle is reduced. By doing this many accidents will reduce and provides safe life to the driver and vehicle safety. A system for driver safety and car security is presented only in the luxurious costly cars. Using drowsiness detection system, driver safety can be implemented in normal cars also. The results showed several dynamic changes of the eyes during the periods of drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers‘ drowsiness by considering the dynamic changes of the eyes.
The aim of this dissertation is to detect the drowsiness for drivers using image processing.We are going to design a system using camera that points directly towards the driver‘s face and monitors the driver‘s eyes in order to detect fatigue or drowsiness by self developed image processing algorithm which can give information regarding drowsiness of drivers. So the first step is the face detection. For face detection viola-jones method is used. Viola-jones has been
successfully applied on the facial detection system and based on the accuracy of human location detection. The second step is Feature Extraction like detect the eye portion which has been done by viola-jones algorithm. During detection of eyes, system will be able to decide if the eyes are open or closed and whether the driver is looking in front by self developed algorithm and its pixels map. When the eyes will be closed for too long, a warning signal will be given in the form of buzzer or in the form of alarm signal and also send the feedback reply to the driver for the system.

Technical Details of Final Deliverable

A proposed system will be able to detect drowsiness and generates an alarm to alert the driver. 

A digital image processing algorithm namely Viola-Jones has been used to detect drowisness in the driver with the help of camera which can take picture of the driver when eye are blinking at a very high speed. 

Raspbeery Pi is used as a microcontroller which is used to control hardware. 

Final Deliverable of the Project HW/SW integrated systemCore Industry TransportationOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Good Health and Well-Being for People, Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	37049
Raspberry Pi	Equipment	1	12599	12599
LogiTech Camera	Equipment	1	9750	9750
Buzzer	Equipment	2	950	1900
Wires and Cables	Equipment	2	150	300
Power Bank 10 mAh	Equipment	1	4500	4500
Prototype Car	Equipment	1	8000	8000