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@article{198259,
        author = {Aditi Tushar Mahadeshwar and Pratik Gulab Mahajan and Chirag Rudrasen Patil and Vivek Sunil Patil and VIJAYLAXMI},
        title = {Real-Time Driver Drowsiness Detection System Using Computer Vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13393-13399},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198259},
        abstract = {Driver drowsiness is a major cause of road accidents and poses a serious threat to road safety. Detecting fatigue at an early stage can help prevent accidents and protect drivers as well as passengers. This paper presents a real-time driver drowsiness detection system based on computer vision techniques. The system monitors the driver’s facial features using a camera and analyzes eye blinking and yawning patterns to identify signs of fatigue. Facial landmark detection is used to track the eyes and mouth, while the Eye Aspect Ratio (EAR) helps determine whether the driver’s eyes are open or closed. When the system detects prolonged eye closure or repeated yawning, it triggers an alert to warn the driver. The proposed system is designed to be simple, cost-effective, and capable of improving road safety by reducing accidents caused by driver fatigue.},
        keywords = {Driver Drowsiness Detection, Computer Vision, Facial Landmark Detection, Eye Aspect Ratio, Machine Learning, Road Safety.},
        month = {April},
        }