Hybrid AI Frameworks for Intelligent Video Surveillance Systems

Hybrid AI Frameworks for Intelligent Video Surveillance Systems explores the integration of artificial intelligence, machine learning, deep learning, and computer vision for the development of intelligent video surveillance systems. The book examines how modern AI techniques can transform conventional video monitoring into automated systems capable of analyzing visual information, recognizing patterns, and supporting real-time decision-making.

The book introduces fundamental concepts in intelligent video analytics, image and video processing, computer vision, machine learning, and deep learning. It discusses hybrid AI frameworks that combine multiple computational approaches to improve the analysis of surveillance footage, including object detection, activity recognition, visual classification, and event analysis.

Particular attention is given to the development of intelligent surveillance pipelines, including video data acquisition, preprocessing, feature extraction, model training, classification, detection, and performance evaluation. The book also considers practical challenges associated with real-world surveillance environments, including large-scale video data, varying lighting conditions, complex scenes, system reliability, and real-time processing requirements.

By connecting hybrid artificial intelligence techniques with intelligent video surveillance, this book provides a useful reference for students, researchers, data scientists, computer vision professionals, security technology specialists, and practitioners working in artificial intelligence and video analytics. It is particularly relevant to readers interested in developing automated and intelligent systems for visual monitoring, event detection, and security-oriented applications.