Survey on intelligent video surveillance

Video surveillance systems have witnessed rapid advancements over the past two decades, fueled by progress in computer vision, artificial intelligence, and edge computing. However, despite extensive research, there remains a need for a review that synthesizes the architectures, methodologies, and evolving trends of intelligent video surveillance systems from a system-level perspective. Most existing literature reviews focus narrowly on individual tasks such as object detection, motion tracking, or anomaly recognition, without addressing how these components integrate within a complete system. This paper addresses this gap by conducting a systematic review of research published between January 2000 and July 2025, drawing from major digital repositories. The main contributions of this work are threefold: (1) it proposes a structured taxonomy of intelligent surveillance systems based on key functionalities, including object detection, motion tracking, abnormal event detection, vehicle and traffic monitoring, object counting, and multi-camera coordination; (2) it provides a comparative analysis of methodologies, datasets, and platforms used across studies; and (3) it explores the interconnections among these core tasks while identifying critical challenges such as real-time processing, privacy preservation, data scalability, and system interoperability. Key outcomes of this review include the identification of underexplored research areas—such as federated learning and cross-camera integration—and practical guidelines for designing next-generation surveillance systems. The literature analyzed was sourced from major digital repositories including IEEE Xplore, ScienceDirect, Google Scholar, arXiv, Springer, and the ACM Digital Library.