JOURNAL ARTICLE

Driver Drowsiness Detection system using opencv and keras

Abstract

Finding sleepy drivers is crucial to maintaining traffic safety.Machine learning techniques have been used in a number of recent proposals to identify driver drowsiness.In this research, we provide a technique for OpenCV and Kerasbased driver drowsiness detection.The suggested technique employs a camera to record a live video feed of the driver's face.The required features, such as eye and mouth movements, are then extracted from the video frames using OpenCV preprocessing.Then, a deep learning model based on the Keras framework is trained using the extracted features.A sizable dataset of films documenting driver attentiveness and tiredness is used to train the model.The model is used to forecast the driver's level of tiredness using the retrieved features after it has been trained.

Keywords:
Artificial intelligence Computer vision Computer science Cartography Geography

Metrics

0
Cited By
0.00
FWCI (Field Weighted Citation Impact)
0
Refs
0.07
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Sleep and Work-Related Fatigue
Social Sciences →  Psychology →  Experimental and Cognitive Psychology
© 2026 ScienceGate Book Chapters — All rights reserved.