A real-time drowsiness detection system for drivers, which alerts the driver if they fall asleep due to fatigue while still driving. The computer vision algorithm used for the implementation uses a trifold approach to detect drowsiness, including the measurement of forward head tilt angle, measurement of eye aspect ratio (to detect closure of eyes) and measurement of mouth aspect ratio (to detect yawning).

AI-powered Driver Drowsiness Detection System using Computer Vision & Machine Learning for real-time driver alertness monitoring and accident prevention.

Driver drowsiness detection is a car safety Technology which helps prevent accidents caused by the driver getting drowsy. The following code uses computer vision to observe the driver's face, either using a built-in cameraor on mobile devices.

# Intellifotainment assist” – Smart HMI for passenger cars To run the program, download all files and save them in the same directory. After that, simply run 'Master.py' in the terminal. At the moment, the program will only run in linux based systems. # The Idea Infotainment systems have come a long way since the first set of dashboards installed in cars. Through our idea, we aim to create a Human Machine interaction model that takes infotainment systems to a new level. The driver tends to get distracted from the road while performing secondary tasks such as changing the music track, locking/unlocking the door while driving etc. Our system shall enable the driver to focus only on driving. Controlling the secondary tasks will be much easier. Our product primarily comprises of 5 modules: 1) Attention and drowsiness detection: - A camera shall be present on the dashboard, in front of the driver, behind the steering wheel. Through digital image processing techniques , using hough circle algorithm and haarcascade of an eye, we shall keep track of the driver’s sight. If he or she is looking away from the road while driving for more than a specified amount of time, we shall alert the driver to focus. We shall map the head orientation and iris position to accurately identify the driver’s attention. 2) Infotainment control features using blink combination: Through a combination of blinks, the driver can turn on or of the headlights, tail lights as well as indicators. Blinking of the eyes shall be detected using ‘dlib’ features in python. This shall give extremely accurate results. 3) Voice commands to control wipers, car lock, music system and windows A simple, yet extremely useful idea that would make the life of the driver a whole lot easy. Enabling the driver to speak to his car infotainment system would allow him to control and navigate these functionalities with great ease. The car will be enabled with a virtual assistant. 4) Automatic rear view mirror adjustment scheme: Using the camera placed in front of the driver, the system shall detect the position of the driver’s head. This shall also be done using image processing techniques and we shall identify the coordinates of the driver head in 3D space. There will be a mapping between the head position and mirror adjustment scheme. The mirrors will adjust their position using servo motors and shall do so automatically by identifying the head position. 5) A revolutionary reverse-assistance algorithm for smart parking and general reversing: Probably the highlight of our model, this feature shall make driving the car in and out of a parking spot, or rather, even reversing a car in general, far easier and safer than what it already is. Like most other modern cars, our model shall also have a camera installed at the back and the corresponding image displayed at the infotainment screen for parking assistance. Upon activating the reverse gear, the screen shall trace the line of motion of the car corresponding to the current position of the steering wheel. Because of this feature, the driver gets an idea of whether or not he’ll hit an obstacle while reversing if the steering wheel is kept at that position. Taking this feature to another level, the rear camera, after capturing the live video feed from the back of the car, shall perform image processing and machine learning algorithms to find a safe, obstacle-free path for reversing and indicate the driver to move the steering wheel accordingly. So instead of relying solely on the drivers judgement, our system shall actually find the path to be taken while reversing, such that other cars and other obstacles will be avoided, and accordingly recommend the driver to steer the wheel in that direction. This feature shall be extremely useful for new drivers/ learners. During the initial phase, to prevent errors from creeping in, we will always have a manual override button. After a good amount of testing, further modifications and refinements can be made. Our systems adds new dimensions to both precautionary safety measures, as well as convenience. If implemented properly, we are confident that our project will reach new heights of HMI and driver assistance technology. It will give drivers several less reasons to worry about.

In this project, we have created a driver drowsiness detection system that will detect whether the driver's eyes are closed for too long and detect whether the driver is sleepy or inactive.

Drowsy Driver Detection System using OpenCV and CNN . A real-time drowsiness detection system that alerts drivers when signs of drowsiness are detected using computer vision and deep learning. This project leverages OpenCV for video capture and CNN for eye state classification.

Driver Drowsiness Detection System for Road Safety

A real-time driver drowsiness detection system using Haar Cascade for face detection, LSTM for sequential analysis, and CNN for feature extraction, achieving 95.1% accuracy. The system monitors eye closure patterns and triggers alerts to prevent accidents and enhance road safety.

Every year many people lose their lives due to fatal road accidents around the world and Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Alcohol, Overwork, Stress, and even Medical conditions can cause drivers to fall sleep. It is very important to detect the drowsiness of the driver to save life and property. So to reduce the accidents and save the life of a driver we propose to develop a system called as Driver Drowsiness Detection (D3 ) system. This system can automatically detect driver drowsiness in a real-time video stream and then play an alarm if the driver appears to be drowsy. Haar Cascade classifier, facial landmarks and computing Eye Aspect Ratio (EAR) to ensure proper detection of drowsiness in order to avoid accidents. For implementing this system we used libraries like Opencv and dlib.

Driver drowsiness is the most critical cause of road accidents so detection of drowsiness play a vital role in preventing road accidents. We are developing an android app that will alert drivers before an accident occurs. This will reduce the number of road accidents on a road. Drowsiness is a natural phenomenon that happens in human body due to different factors. Machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. In this app, front camera will take a picture of drowsy driver then this picture will be taken as input. In processing the detected image, we are using OpenCV Library. OpenCV Library uses Haar Cascade Classifier for detection images such as eyes and face. Eyes and face will be the target in this system. This application will be implemented on Android Operating System. Drowsiness detection system will send alert to the driver when the driver feels asleep while driving a car, this can avoid accidents. Driver which is the user in this application, if they close their eyes within one second, the sensor which is the front camera in the smartphone will catch and process this event and then trigger the system to give voice alert to the user. Moreover, if the driver is willing to turn on back camera then it will detect the lane detection violation and will calculate the distance from the vehicle ahead of it. If the distance is too close, then it will generate an alarm. It will also generate an alarm if there is a violation of the lane on the road.

This repo contains the source code for smart driver monitoring system. Currently, it supports Object detection such as mobile handset, wine glass, bottle etc., Head-pose estimation, drowsiness detection, iris landmarks estimation.

Real-time driver drowsiness detection system using computer vision, OpenCV, and MediaPipe face mesh. Monitors eye movements and calculates Eye Aspect Ratio (EAR) to detect fatigue, providing immediate visual alerts for enhanced driving safety.

Driver Drowsiness System intermediate-level Python 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 I build a system using Python, OpenCV and Arduino which will alert the driver when he feels sleepy. Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving. The objective of this intermediate Python project is to build a drowsiness detection system that will detect that a person’s eyes are closed for a few seconds. This system will alert the driver when drowsiness is detected.

Here in the project, we will use the python language along with the OpenCV library for the algorithm execution and image processing respectively. The haar cascades we are going to use in the project are pretrained and stored along with the OpenCV library as haarcascade_frontalface_default.xml and haarcascade_eye_tree_eyeglasses.xml files. The project develops a basic understanding of the systems such as driver drowsiness detection, eye blink locks, eye detection, face detection and also the haar cascades usage with the OpenCV library.

In this repository you will find an efficient 'Real Time Driver Drowsiness Detection for an Intelligent Transportation System', that will work on various constraints like while wearing Eye Glasses, Mask etc.

SomnoGuard is a real-time drowsiness and fatigue detection system for drivers. Using deep learning algorithms, it monitors the driver's eyes and yawning patterns to detect signs of drowsiness and fatigue. The system provides real-time alerts to drivers, aiming to reduce the risk of accidents caused by driver fatigue.

Real-time driver drowsiness detection system using TensorFlow and OpenCV, leveraging a fine-tuned MobileNet CNN to monitor eye closure and trigger low-latency alerts for enhanced road safety.

This project is to develop a system that can detect the drowsiness of the driver and make alarms accordingly using Computer Vision. It is developed in MATLAB and we have used image processing algorithm also known as Viola Jones algorithm for detection of faces and eyes, the image of eye is continously processed and the ratio of black pixels to white pixels is calculated and based on the bw ratio, the alarm beeps if driver is asleep.

This project is a Drowsiness Detection Dashboard that uses computer vision and AI to monitor a driver’s alertness in real time. It employs OpenCV and MediaPipe Face Mesh to analyze eye and mouth movements, calculating EAR (Eye Aspect Ratio) and MAR (Mouth Aspect Ratio) to detect signs of sleepiness or yawning.

Research and development of a drowsiness detection system for drivers using behavioural, physiological and vehicular features.

Drowsiness detection and alert system for vehicle drivers. Developed in Python language using OpenCV and dlib libraries

This repository contains a Jupyter Notebook (driver-drowsiness-detection-system.ipynb) that implements a driver drowsiness detection system using machine learning techniques. The system preprocesses images, trains convolutional neural network (CNN) models for classification, evaluates their performance, and predicts drowsiness based on input images

I have developed a Drowsiness detection system specially for drivers. (If they look aside, close eyes, feeling sleepy) this system alert them with Beep sound

This repository contains the code and documentation for a Driver Drowsiness Detection System implemented using Arduino. The system is designed to monitor the driver's level of alertness and provide warnings if drowsiness is detected, thereby helping to prevent accidents caused by driver fatigue.

Driver Drowsiness Detection System using Mediapipe and deep learning combines real-time facial landmark tracking, feature extraction, and a trained neural network to monitor drivers for signs of drowsiness. This technology contributes to road safety by helping to prevent accidents caused by driver fatigue.

The Drowsiness Detection System is a saftey technology that can prevent accidents by detecting a person's eyes are closed for a few seconds, This will alert the driver when drowsiness is detected.

Drowsiness detection and alert system(DDAS) is an electronic device for the safety of the people who travel and drive. Nowadays there are several types of sensors and navigation systems available in the market for safe driving. One mistake of the driver can lead to severe damages or physical injuries, deaths and significant economic losses. The idea of developing such a device arises from several aspects concerning about the safety of the people and economic losses. There are various reasons especially human errors which gives rises to the road accidents. The recent reports say that there is a huge increment in the road accidents in every country since the last few years. The main reason occurring from the highway accidents is the drowsiness and sleepiness of driver while driving. The long route drivers can sometimes asleep while driving, which is dangerous for everyone with him. It is a necessary step to come with an efficient technique to detect drowsiness as soon as the driver feels sleepy and give an alert before any could go wrong.

Sleep Detector is an AI-powered system that detects driver drowsiness in real time using deep learning and computer vision. It analyzes facial features from a webcam to classify drivers as drowsy or alert, helping prevent fatigue-related accidents. Built with Keras and TensorFlow, the model is trained on a labeled dataset for accurate detection.

The "Drowsiness Detection and Alerting System" is an innovative project that harnesses the power of modern technology to enhance road safety and mitigate the risks associated with driver fatigue. By combining Python programming, IBM Cloud services, OpenCV, and Node-RED, this system offers a comprehensive solution for monitoring a driver's alertness

Driver Drowsiness detection is a safety technology that can prevent accidents that are  caused by drivers who fell asleep while driving or having a fatigue. So, to prevent these  accidents we will build a system using Python, OpenCV, and Keras which will alert the  driver when he feels sleepy. This system will detect that whether person eyes are closed  or open and depending upon the result if it’s closed for few seconds the system will  alert the driver immediately by beeping the alarm.