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@article{200087,
        author = {Dr. M.Karthikeyan and Prathap P and Nivas S and Naveen Kumar M and Yakash G},
        title = {IoT-Driven Edge Vision System for Real-Time Attendance Tracking Using Raspberry Pi},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {4091-4097},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200087},
        abstract = {This paper presents an IoT-driven edge vision system for real-time attendance tracking using a Raspberry Pi-based camera module. The proposed system performs on-device image capture and processing to detect and recognize individuals, enabling automated attendance marking with reduced latency and minimal reliance on cloud infrastructure. By integrating embedded vision with IoT communication protocols, the system ensures efficient data transmission and remote accessibility. Experimental results demonstrate reliable performance in terms of accuracy and response time under varying conditions. The solution offers a cost-effective, scalable, and efficient approach for automated attendance monitoring in educational and organizational environments.},
        keywords = {Internet of Things (IoT), Edge Computing, Raspberry Pi, Embedded Vision, Attendance Monitoring, Real-Time System, Image Processing, Automation.},
        month = {May},
        }