• Revathi Chitra
• Niranjan K

This paper introduces the core methodology and experimental foundation of the Adaptive AI-Driven Surveillance (AADS) framework, designed to address critical limitations in conventional video surveillance systems. By combining advanced object detection, anomaly detection, and multi-sensor fusion, this framework aims to deliver real-time, scalable, and context-aware security solutions. The section details the underlying models, algorithms, and experimental setups used to validate the system's performance in diverse environments. Through comprehensive analysis, the section demonstrates how the proposed approach enhances detection accuracy, minimizes false alarms, and ensures efficient monitoring, paving the way for robust, real-time threat identification and proactive decision-making. The AADS framework represents a major step forward in intelligent surveillance, blending adaptability, efficiency, and ethical considerations. Through its robust design and validation across multiple scenarios, the system promises significant contributions to public safety, operational efficiency, and overall security.

smart visual sensors; surveillance; intelligent detection; security

"Advanced Alternative Approaches in Natural Language Processing for Translation and Information Dissemination Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.a395-a404, March-2025, Available :http://www.jetir.org/papers/JETIR2503050.pdf

"Advanced Alternative Approaches in Natural Language Processing for Translation and Information Dissemination Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppa395-a404, March-2025, Available at : http://www.jetir.org/papers/JETIR2503050.pdf