Published Paper Details:

The goal of the "Driver Distraction Detection" project is to use computer vision and machine learning techniques to create a reliable system for the real-time detection of distracted driving behavior. Road safety is significantly threatened by distracted driving, which results in a high number of mishaps worldwide. This concept uses the on-board cameras in cars to continuously monitor driver behavior to address this problem. To locate the driver's face within the camera frame and identify important facial landmarks, the system uses tracking and facial detection algorithms. The technology determines the driver's attention level and detects indications of distraction by eye gaze analysis, eye blinking patterns tracking, and head position estimate. Using labeled data, machine learning models are trained to categorize driver behavior into focused and distracted categories based on attributes that are retrieved. The successful implementation of the "Driver Distraction Detection" project holds the potential to significantly enhance road safety by reducing the prevalence of accidents caused by distracted driving. This project aligns with broader efforts to leverage technology to mitigate risks on the road and create safer driving environments for all road users.