Introduction to Intelligent Surveillance

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This accessible textbook/reference reviews the fundamental concepts and practical issues in designing digital surveillance systems that leverage intelligent computing techniques. It covers all aspects, from camera calibration and data capture to secure transmission of surveillance data. The text addresses object and biometric feature detection and recognition, automated observation of surveillance events, and enhancements through deep learning and supercomputing technology. This updated edition features expanded coverage on face detection, pedestrian detection, and privacy preservation. Key topics include review questions and exercises in each chapter, a glossary, and essentials for implementing intelligent surveillance systems and analyzing biometric data. It emphasizes network security, digital forensics, and ethical issues in data communication. The book discusses the Viola-Jones object detection method and the HOG algorithm for recognizing pedestrian and human behavior. It also explores artificial intelligence for automated monitoring and decision-making regarding human intervention. A case study illustrates a system that triggers an alarm for vehicles failing to stop at red lights and identifies license plates. Additionally, it investigates cutting-edge supercomputing technologies like FPGA, GPU, and parallel computing. This concise, classroom-tested resource is ideal for undergraduate and postgraduate courses and serves