DRIVEEYE: Smart Driver Safety Monitoring Using Real-Time Computer Vision

Driver drowsiness is a significant road-safety concern because reduced alertness can affect attention, reaction time and vehicle control. This paper presents DRIVEEYE, a low-cost real-time driver safety monitoring prototype that observes visible signs of drowsiness using a camera. The current system uses a laptop webcam and computer-vision processing based on OpenCV and MediaPipe facial landmarks. It monitors prolonged eye closure and head position and generates an audio warning when the defined drowsiness condition is reached. A time-based threshold is used so that ordinary short blinking is not treated as drowsiness. The paper describes the system architecture, workflow, detection logic, qualitative evaluation and proposed hardware integration. The future system is intended to use a compact processing unit, driver-facing camera, speaker and rear-facing road camera. The prototype demonstrates the feasibility of a simple non-contact approach while also identifying the need for broader validation before any safety-critical deployment.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-11
DOI
https://doi.org/10.5281/zenodo.24129890
Primary Topic
Sleep and Work-Related Fatigue
Type
preprint
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preprint

DRIVEEYE: Smart Driver Safety Monitoring Using Real-Time Computer Vision

Susheel Kumar S, Arif Shaik, Abdul Amaan Shaik, Vasanth Vakiti
Zenodo (CERN European Organization for Nuclear Research)
Sleep and Work-Related Fatigue
preprint

DRIVEEYE: Smart Driver Safety Monitoring Using Real-Time Computer Vision

Susheel Kumar S, Arif Shaik, Abdul Amaan Shaik, Vasanth Vakiti
preprint en

Abstract

Driver drowsiness is a significant road-safety concern because reduced alertness can affect attention, reaction time and vehicle control. This paper presents DRIVEEYE, a low-cost real-time driver safety monitoring prototype that observes visible signs of drowsiness using a camera. The current system uses a laptop webcam and computer-vision processing based on OpenCV and MediaPipe facial landmarks. It monitors prolonged eye closure and head position and generates an audio warning when the defined drowsiness condition is reached. A time-based threshold is used so that ordinary short blinking is not treated as drowsiness. The paper describes the system architecture, workflow, detection logic, qualitative evaluation and proposed hardware integration. The future system is intended to use a compact processing unit, driver-facing camera, speaker and rear-facing road camera. The prototype demonstrates the feasibility of a simple non-contact approach while also identifying the need for broader validation before any safety-critical deployment.

Zenodo (CERN European Organization for Nuclear Research)
Sleep and Work-Related Fatigue
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