This concise textbook/reference examines the fundamental aspects of intelligent computing for surveillance systems, from camera calibration and data capturing, to secure data transmission. The text covers digital surveillance from the level of an individual object or biometric feature, to the full lifecycle of an event. This is followed by a detailed discussion on how an intelligent system can independently monitor and learn from an event, and invite human input when necessary. The book concludes with a presentation on how the computation speed of the system can be enhanced through the use of supercomputing technology.

Topics and features:

• Contains exercises at the end of every chapter, and a glossary of important terms
• Provides a thorough introduction to the fundamentals of intelligent surveillance, including surveillance data capture and surveillance data compression
• Covers the key issues of computer network infrastructure, security, monitoring and forensics, and the essential aspects of object analysis
• Presents a detailed review of algorithms for surveillance data analytics using biometric features
• Introduces the concept of surveillance events, and discusses how artificial intelligence can be used for the automated observation and understanding of such events
• Reviews algorithms that apply decision-making approaches to determine the need for triggering an alarm to alert a member of security staff
• Describes the use of cutting-edge supercomputing technologies for digital surveillance, such as FPGA, GPU and parallel computing

Dr. Wei Qi Yan is a Senior Lecturer in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title Visual Cryptography for Image Processing and Security.

This concise textbook examines the fundamental aspects of intelligent computing for surveillance systems, from camera calibration and data capturing, to secure data transmission. The text covers digital surveillance from the level of an individual object or biometric feature, to the full lifecycle of an event. This is followed by a detailed discussion on how an intelligent system can independently monitor and learn from an event, and invite human input when necessary. The book concludes with a presentation on how the system can be enhanced through the use of supercomputing technology. Features: contains exercises at the end of every chapter, and a glossary; covers the key issues of computer network infrastructure, security, monitoring and forensics, and the essential aspects of object analysis; reviews algorithms for surveillance data analytics using biometric features; discusses the use of AI for surveillance events; reviews algorithms that trigger an alarm to alert a member of security staff.

Introduction

Surveillance Data Capturing and Compression

Surveillance Data Secure Transmissions

Surveillance Data Analytics

Biometrics for Surveillance

Visual Event Computing I

Visual Event Computing II

Surveillance Alarm Making

Surveillance Computing