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JayanthSD2003 / repository
This is a project which can be seen on TATA Trucks and other premium edition cars based and focussed on the safety and monitoring driver Consciousness state
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SARATHI (Safety Assisted Responsive Automated Technology for Highway Independance)
The SARATHI (Safety Assisted Responsive Automated Technology for Highway Independance) - Driver Monitoring System (DMS) leverages computer vision and machine learning to detect driver drowsiness in real-time using webcam video feeds. The system is constructed using several key technologies:
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WorkofAditya /
This is a face recognition program that uses a webcam feed to detect and recognize faces.
63/100 healthMpradeep-dev /
This project is a real-time drowsiness detection system designed to monitor a user's eye activity and alert them if signs of drowsiness or sleep are detected. It leverages computer vision techniques and facial landmark detection to analyze eye blinking patterns, ensuring the safety of drivers or individuals performing critical tasks.
55/100 healthThe EAR is computed by detecting the coordinates of the eyes and calculating the distances between specific points. The formula is:
where A and B are the vertical distances and C is the horizontal distance.
NOTE: Download the shape_predictor_68_face_landmarks.dat file from the following link: https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat
The Driver Monitoring System is designed to help drivers stay awake and alert while on the road. Here's how it works:
The system uses a webcam to watch the driver’s face in real-time. It looks for signs that the driver might be getting sleepy.
The system not only plays a sound to wake you up if you’re drowsy but also uses a voice alert saying "Wake up!" to ensure you don’t miss the warning.
This system is built using various technologies like computer vision for face and eye detection, sound alerts to wake you up, and a text-to-speech engine to provide voice warnings. It’s all about keeping drivers safe by ensuring they stay awake and alert while driving.
ultron1101 /
This is a prototype of the drowsiness detection model that just detects the EAR (Eye Aspect Ratio) of the eye to tell if the driver is drowsy or not. It uses the dlib library to extract facial landmarks.
31/100 healthEmotion detector capable of identifying 7 of the most important human emotions: angriness, disgust, fear, happiness, neutral, sadness, surprise. This application is based on the 68 facial landmarks computed by the shape predictor from "dlib" Python module. The model for this deep learning application is built using Tensorflow Keras and was trained on the FER-2013 dataset. The dataset is parsed using Pandas module of Python.
48/100 healthThis project implements a Drowsiness Detection System using Python, OpenCV, and dlib. The system monitors a user's eye aspect ratio (EAR) through a webcam feed to detect signs of drowsiness, alerting the user with an alarm sound when drowsiness is detected.
40/100 healthMilindddd /
A real-time Drowsiness Detection System built using OpenCV, Dlib, and machine learning techniques. This project is designed to monitor driver alertness by detecting signs of drowsiness such as prolonged eye closure and yawning, aiming to reduce road accidents caused by fatigue.
31/100 health