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@article{174160,
        author = {Y Vineeth},
        title = {Integrating Computer Vision and Natural Language Processing for Minimizing and Detecting Collisions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3404-3406},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174160},
        abstract = {Road accidents are a leading cause of death worldwide. This paper introduces an innovative tracking system that integrates Computer Vision (CV) and Natural Language Processing (NLP) to detect and minimize collisions. A chip embedded with ultrasonic sensors transmits data directly to emergency services in case of accidents. Proactive measures, such as monitoring driver behavior using physiological and behavioral cues (eye tracking, heart rate monitoring, and PERCLOS), are employed to prevent drowsy driving. NLP-based virtual assistants provide real-time auditory alerts to drivers. By combining CV and NLP, this system enhances collision prevention, improves survival rates, and ensures prompt emergency responses.},
        keywords = {Road Accident Detection, Collision Detection System, Tracking Device, Computer Vision, Hough Transform, Natural Language Processing, Automated Emergency Response, Survival Rate Improvement.},
        month = {March},
        }