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@article{169320,
        author = {Shreyas Ghansawant and Atharva Thokal and Tanishq Ladde and Prof. Nikita Khawase},
        title = {AI-Powered Video Surveillance for Enhanced Object Detection and Incident Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1410-1414},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169320},
        abstract = {This paper presents a comprehensive analysis of AI-powered video surveillance systems designed to enhance object detection and incident monitoring, with a particular focus on real-time identification of critical events such as vehicle collisions and human falls. With the growing demand for intelligent monitoring systems, this study addresses the limitations of traditional surveillance methods by integrating machine learning models like YOLO (You Only Look Once) to improve detection accuracy and efficiency. The proposed system employs advanced deep learning techniques for real-time threat and incident recognition, leading to a significant reduction in response times. We evaluate the system's performance in diverse scenarios and explore its potential for large-scale implementation within smart city environments. Our findings demonstrate improved accuracy in incident detection while minimizing false alarms, contributing to more reliable and automated surveillance systems.},
        keywords = {},
        month = {November},
        }