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@article{190835,
        author = {Sarika S. Kamble and Balaji A. Chaugule},
        title = {Comprehensive Review on Robust VideoGuard System for Urban Surveillance and Incident Analysis Using AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5253-5258},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190835},
        abstract = {Urban surveillance systems generate massive video data that exceed human monitoring capabilities, necessitating intelligent and automated analysis. Recent advances in artificial intelligence, deep learning, and edge–cloud computing have significantly improved real-time incident detection, anomaly recognition, and situational awareness in smart cities. However, existing solutions often suffer from limitations related to scalability, latency, privacy preservation, and unified system design. Motivated by these challenges, this paper explores the development of a Robust VideoGuard System that integrates AI-driven video analytics, edge intelligence, and privacy-aware learning for urban surveillance. The proposed approach aims to enable efficient, reliable, and ethical incident analysis for enhanced public safety.},
        keywords = {Urban Surveillance, Artificial Intelligence (AI), Video Analytics, Anomaly Detection, Edge–Cloud Computing, Privacy-Preserving Surveillance},
        month = {January},
        }