Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{194846,
        author = {Joe-Uzuegbu C. K and Ezigbo P. J. and Aniugo V. O and Umerah A.T and Imoke G. A and Mbonu S. E and Ogomaka C. C},
        title = {DESIGN AND INSTALLATION OF A SMART SURVEILLANCE SYSTEM FOR THEFT DETECTION, TRACKING AND MONITORING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8247-8259},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194846},
        abstract = {This paper presents the design and implementation of a smart surveillance system that combines local DVR-based monitoring with AI-enabled cloud intelligence. The system integrates motion-based anomaly detection with cloud-based event prioritization to address the limitations of traditional DVR-only setups. A prototype was implemented using IP cameras, AI event filters, and a hybrid storage framework. Over a 72-hour validation period, the system achieved a 92% precision rate and 88% recall in anomaly detection, while maintaining 99.3% system uptime. The smart surveillance model reduces unnecessary storage loads by 83% through selective cloud uploads, and includes built-in failover power support. Raw system logs, confusion matrix analysis, and precision-recall curves validate the system's performance. This approach offers a cost-effective and scalable upgrade path for existing surveillance infrastructure, enhancing security while minimizing bandwidth and maintenance overhead.},
        keywords = {},
        month = {March},
        }