A MATHEMATICAL MODELING PERSPECTIVE FOR AUTOMATION ON IDEAL SELF-REGULATING VIDEO SURVEILLANCE SYSTEMS

Abstract

This study introduces a self-regulating video surveillance framework that draws upon mathematical modeling to integrate artificial intelligence, machine learning, and computer vision for enhanced automation. The proposed system is designed to operate with minimal human intervention by autonomously processing live video streams, identifying anomalous events, and dynamically adjusting monitoring parameters. A layered computational strategy is employed, where edge devices handle low-latency recognition tasks and cloud platforms manage large-scale training and optimization. This division of workload improves scalability, ensures efficient resource utilization, and maintains real-time responsiveness. The research emphasizes a formal mathematical representation of data flows, learning functions, and optimization routines, providing a structured basis for adaptive monitoring. Privacy-preserving techniques, including federated learning and blockchain integration, are incorporated to address regulatory and ethical considerations, ensuring secure and transparent data handling. Experimental evaluation highlights improvements in detection accuracy, reduction of false alarms, and efficient distribution of computational resources. By presenting a unified mathematical and algorithmic perspective, this work advances the design of intelligent surveillance systems applicable to smart cities, industrial environments, and critical infrastructure protection. The proposed approach not only enhances reliability and adaptability but also establishes a foundation for future extensions involving autonomous learning and decentralized surveillance architectures. Mathematics Subject Classification (2020): • 68T07 Artificial neural networks and deep learning • 68U10 Image processing and computer vision • 68M14 Distributed systems • 90C90 Applications of mathematical programming • 94A08 Image and video processing.