Information technology for decision support in the field of video surveillance

Abstract

The article explores the concept of developing a modern information technology system designed to provide effective decision support in the field of video surveillance. Given the in-creasing volume of video data generated by monitoring systems, especially in urban environ-ments, there is a growing need for high-performance solutions capable of operating in real time. The focus is placed on analyzing technological approaches to video stream processing using artificial intelligence, machine learning methods, edge computing, and cloud-based so-lutions. The study presents an architectural model of a multi-level information system that includes modules for data collection, preprocessing, analytics, and decision-making. Special attention is given to event detection algorithms and the classification of behavioral patterns, which helps reduce dependence on human intervention during surveillance analysis.
The paper also examines aspects of integrating such systems with other information-analytical platforms, including biometric databases, geographic information systems, and ac-cess control systems. The advantages of edge architectures are substantiated, as they reduce video processing latency, enhance system autonomy, and lower the load on central infrastruc-ture. The research findings can be applied to the development of next-generation intelligent security systems, particularly within the framework of the Smart City concept. The proposed approach can be adapted to meet the needs of local authorities, law enforcement agencies, and private sector entities involved in the protection of critical infrastructure. The results demonstrate the practical value of implementing advanced IT solutions in the domains of se-curity and monitoring.