USING ARTIFICIAL INTELLIGENCE SYSTEMS TO ENHANCE PUBLIC SAFETY BASED ON VIDEO STREAM ANALYSIS

Abstract

In the context of rapid urbanization and increasing population density, ensuring public safety in urban environments has become a critically important task. Traditional video surveillance systems, which rely heavily on human operators, are often unable to efficiently process large volumes of video data in real time, reducing the effectiveness of timely threat detection and response. In this regard, the use of artificial intelligence systems capable of automatically analyzing video streams offers significant potential for improving public safety.

This paper explores approaches to applying computer vision and machine learning techniques to enhance the effectiveness of surveillance systems. Particular attention is given to technologies for object detection and classification, as well as action and behavior recognition, including the identification of aggressive behavior, physical altercations, and other potentially dangerous activities. The study also outlines the key stages involved in developing intelligent video analysis systems, including data collection and preprocessing, model training, and deployment in real-world environments.

In addition, the advantages of such systems are analyzed, including reduced workload for human operators, improved accuracy in incident detection, and scalability across large camera networks. Special consideration is given to challenges such as minimizing false positives, ensuring system reliability, and adapting models to varying video conditions, including lighting, camera angles, and image quality.

The results demonstrate that integrating artificial intelligence into video surveillance systems significantly enhances monitoring efficiency and enables faster response to emerging threats. The paper concludes by highlighting the future development potential of these technologies and their important role in creating safer urban environments.