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).

This is my First project done by me and my friends in College First SEM.

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

No description available

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.

CS-GY 6953 Deep Learning Major Project

Driver Drowsiness Detection System

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.

Driver Drowsiness Detection System with OpenCV & Keras

Driver drowsiness detection system using Computer Vision

No description available

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.

A tool to detect the driver face recognition and alert the driver with voice commands

No description available

Real-time AI Driver Drowsiness Detection using Flask, OpenCV & Dlib with live browser camera monitoring and fatigue alerts.

Driver Drowsiness Detection with YOLOv8 and Facial Features Combat driver fatigue with this deep learning-powered system that utilizes YOLOv8 to detect open and closed eyes, accurately assessing drowsiness levels.

I built a simple driver drowsiness detection system from scratch that uses computer vision to track blink counts and detect prolonged eye closure, triggering an alert!

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.

The driver's drowsiness detection system using TensorFlow and CNN models employs computer vision to analyze real-time images , allowing it to identify signs of driver fatigue and alertness and providing the driver with timely warnings.

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.

Driver drowsiness detection and alerting system

Driver Drowsiness Detection System - Python-based Desktop application that uses computer vision and machine learning techniques to detect signs of drowsiness in drivers

The Driver Drowsiness Detection System uses computer vision to monitor facial landmarks and blinking patterns in real-time, alerting drivers to their alertness status to prevent accidents due to drowsiness.

This project improves road safety with an Driver Assistance System (DAS) that includes drowsiness alerting, lane detection and keeping, object detection, and collision warning. Utilizing YOLO and OpenCV, it monitors drivers and surroundings to enhance safety and prevent accidents.