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@article{188394,
        author = {Mr. Mirza Uzaif Baig and Mr. Mohammed Badaruddin and Mr. Mohammed Abdul Hafeez and Mr. Manish Heble and Prof. Santoshi Pujari},
        title = {THE “RULE COP”},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1719-1722},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188394},
        abstract = {This work presents Rule Cop, an AI-powered surveillance system designed to automate discipline monitoring in educational campuses. Traditional manual supervision suffers from inconsistency, human bias, and limited monitoring coverage. To overcome these challenges, the proposed system integrates YOLOv8 object detection, OpenCV-based video analytics, face recognition, and an SQL database to detect violations such as missing ID cards, dress-code breaches, loitering, and unauthorized entry. The system processes live CCTV feeds, identifies individuals with high accuracy, and stores violation logs with timestamps, snapshots, and location metadata. A dashboard interface enables authorities to review real-time alerts and historical data. Experimental evaluation demonstrates an overall detection accuracy of approximately 92%, validating the system’s ability to deliver scalable, unbiased, and automated monitoring across large campus environments. The approach also provides a foundation for future extensions including automated attendance, anomaly detection, and cloud-integrated analytics},
        keywords = {},
        month = {December},
        }