Save Vision: Smart Drive Monitor

Authors: Deeya Kharediya, Ayushi Gupta, Ankita Shrivastava, Manish Kr. Suman

Abstract: Driver fatigue, drowsiness, and cognitive distraction are among the most underreported yet dangerous causes of road accidents globally. Unlike impairment from alcohol or mechanical failure, these states are invisible to external observers and develop gradually, leaving little time for corrective action once a critical threshold is crossed. Despite advancements in vehicle safety engineering, a reliable, affordable, and non-intrusive system capable of detecting these states in real time remains largely inaccessible to everyday drivers — particularly in developing nations where road fatality rates continue to rise. Save Vision: Smart Drive Monitor is a camera-based, real-time driver monitoring system developed to bridge this gap. The system continuously captures the driver's facial region through a webcam and applies computer vision techniques to analyse four behaviorally significant risk indicators: prolonged eye closure as a marker of drowsiness, yawning as an early physiological signal of fatigue, head pose deviation as an indicator of distraction,and mobile phone usage as a documented cause of inattention. Detection is performed through facial landmark extraction, Eye Aspect Ratio (EAR) computation, and image-based classification signal of fatigue, head pose deviation as an indicator of distraction,and mobile phone usage as a documented cause of inattention. Detection is performed through facial landmark extraction, Eye Aspect Ratio (EAR) computation, and image-based classification, all processed entirely through software. Upon identifying a risk condition, the system activates a layered alert mechanism — combining an auditory alert through the system speaker and an on-screen visual warning — designed to capture the driver's attention proportionally to the severity of the detected state. The system accepts both live webcam input and pre-recorded video, making it flexible for real-world deployment as well as controlled testing environments. Save Vision is designed with accessibility and practicality at its core, targeting deployment without dependency on expensive hardware or cloud infrastructure. The system establishes a functional foundation for intelligent, preventive road safety — one that can be incrementally extended toward integration with advanced driver assistance systems and fleet-level monitoring platforms. By addressing driver risk at its earliest behavioral signs, Save Vision aims to contribute meaningfully to the reduction of road accidents caused by human inattention.

DOI: https://doi.org/10.5281/zenodo.20390165