real time persons drowsiness detection through deep learning7188

This project  can detect your eyes and alert when the user is drowsy. The project made in the field of computer engineering to develop a system for

Project Title

Project Area of Specialization

Artificial Intelligence

Project Summary

Summary:

This project  can detect your eyes and alert when the user is drowsy.

The project made in the field of computer engineering to develop a system for driver or person drowsiness detection to prevent accidents from happening because of driver fatigue and drowsiness. The report proposed the results and solutions on the limited implementation of the various techniques that are introduced in the project.

Whereas the implementation of the project give the real world idea of how the system works and what changes can be done in order to improve the utility of the overall system.

Furthermore, the paper states the overview of the observations made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a safer road or also different situations for keep alert from drowsiness

 This project  can detect your eyes and alert when the user is drowsy.

The project made in the field of computer engineering to develop a system for driver or person drowsiness detection to prevent accidents from happening because of driver fatigue and drowsiness. The report proposed the results and solutions on the limited implementation of the various techniques that are introduced in the project.

Whereas the implementation of the project give the real world idea of how the system works and what changes can be done in order to improve the utility of the overall system.

Furthermore, the paper states the overview of the observations made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a safer road or also different situations for keep alert from drowsiness.

This project can help to save lives and prevent accidents using deep learning. when a driver does not sleep properly or sleep while driving it gives a warning and start alarm which can prevent accidents. A camera in front of the driver face which detects the driver's face and using deep learning identify the driver eyes closed or opened with the help of this data system detect driver drowsiness.

Convolutional Neural Network used for classification of the images, rather person eye in image close or open.Keras API used for building a CNN. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.

Project Objectives

OBJECTIVE:

To identify the driver drowsing detection on image/video stream with the help of computer vision and deep learning algorithm by using the library.

Introduction:

 This project  can detect your eyes and alert when the user is drowsy.

The project made in the field of computer engineering to develop a system for driver or person drowsiness detection to prevent accidents from happening because of driver fatigue and drowsiness. The report proposed the results and solutions on the limited implementation of the various techniques that are introduced in the project.

Whereas the implementation of the project give the real world idea of how the system works and what changes can be done in order to improve the utility of the overall system.

Furthermore, the paper states the overview of the observations made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a safer road or also different situations for keep alert from drowsiness

Why we need this project:

A countless number of people drive on the highway day and night. Taxi drivers, bus drivers, truck drivers and people traveling long-distance suffer from lack of sleep. Due to which it becomes very dangerous to drive when feeling sleepy.

The majority of accidents happen due to the drowsiness of the driver. So, to prevent these accidents we will build a system using Python, OpenCV, and Keras which will alert the driver when he feels sleepy.it can detect yawn, eye blink rate, drowsiness etc.

Application:

This can be used by riders who tend to driver for a longer period of time that may lead accidents or person who wants to do his work but feel drowsing and wants to keep himself alert can use this code to keep himself awake and attentive.

Haar Cascade classifier used for face detection and then extract the face from image. After the face extraction from image we provide this image to train CNN classifier which classify the eyes in the image of the face is close or open. Haar Cascade is a machine learning object detection algorithm used to identify objects in an image or video and based on the concept of feature.Haar Cascade classifier is based on the Haar Wavelet technique to analyse pixels in the image into squares by function.

Project Implementation Method

Implementation:

• Fatigue  and drowsing is a safety problem that has not yet been deeply tackled by any country in the world mainly because of its nature.
• Fatigue, in general, is very difficult to measure or observe unlike alcohol and drugs, which have clear key indicators and tests that are available easily.
• Probably, the best solutions to this problem are awareness about fatigue-related accidents and promoting drivers to admit fatigue when needed.
• The former is hard and much more expensive to achieve, and the latter is not possible without the former as driving for long hours is very lucrative contact numbers or email addresses in the system to send an alert to the one who has not worn the mask. You can also send an alert to the person whose face is not recognizable in the system.

Implementation Method:

In this Python project, we will be using OpenCV for gathering the images from webcam and feed them into a Deep Learning model which will classify whether the person’s eyes are ‘Open’ or ‘Closed’. The approach we will be using for this Python project is as follows :

Step 1 – Take image as input from a camera.

Step 2 – Detect the face in the image and create a Region of Interest (ROI).

Step 3 – Detect the eyes from ROI and feed it to the classifier.

Step 4 – Classifier will categorize whether eyes are open or closed.

Step 5 – Calculate score to check whether the person is drowsy.

Benefits of the Project

A huge benefit is our system is so efficient that we don't need any person on image/video stream drowsy or not.In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures:

Vehicle-based measures:
Another method to measure driver drowsiness involves vehicle-based measurements. In most cases, these measurements are determined in a simulated environment by placing sensors on various vehicle components, including the steering wheel and the acceleration pedal; the signals sent by the sensors are then analyzed to determine the level of drowsiness.

Behavioural measures:
Drowsy behavior is more likely to occur in sleep-deprived drivers. Individuals’ drowsy behavior detection technology should be developed to prevent drowsiness related crashes. Driving information such as acceleration, steering angle and velocity, and physiological signals of drivers such as electroencephalogram (EEG), and eye tracking are adopted in present drowsy behavior detection technologies. However, it is difficult to measure physiological signal, and eye tracking requires complex experiment equipment. As a result, driving information is adopted for drowsy driving detection. In order to achieve this purpose, driving experiment is performed for obtaining driving information through driving simulator.

Physiological measures:
Driver Physiological Measures, Physiological signals are considered to provide an accurate measure of drowsiness because of their strong relationship with driver fatigue . Electroencephalography (EEG) is considered one of the most reliable methods for drowsiness detection.

Technical Details of Final Deliverable

Technical Details of Final Deliverable

OS:

Program is tested on Windows 10 build 1903

Laptop:

Used to run our code.

Webcam:

Used to get the video feed.  

Video Stream:

Optimum resolution for the module's operation:

• HD or Full HD. Framerate: 10 fps or more.
• No mirrored (horizontally flipped) stream.

Illumination and image quality

Illumination of faces in the frame must be uniform and constant. If the camera is installed opposite a bright source of light (sun behind the entrance door, etc.), it is required to adjust the exposure or brightness in such a way that the face in the frame is light. The overexposed background is acceptable. The image quality must be medium or better. Significant compression artefacts are not acceptable. No blurring of moving people's faces is allowed. The image must be in colour.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Peace and Justice Strong Institutions, Partnerships to achieve the Goal

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com