This practically-oriented textbook introduces the fundamentals of designing digital
surveillance systems powered by intelligent computing techniques. The text offers comprehensive
coverage of each aspect of the system from camera calibration and data capture to the secure
transmission of surveillance data in addition to the detection and recognition of individual
biometric features and objects. The coverage concludes with the development of a complete
system for the automated observation of the full lifecycle of a surveillance event enhanced by
the use of artificial intelligence and supercomputing technology. This updated third edition
presents an expanded focus on human behavior analysis and privacy preservation as well as deep
learning methods. Topics and features: contains review questions and exercises in every chapter
together with a glossary describes the essentials of implementing an intelligent surveillance
system and analyzing surveillance data including a range of biometric characteristics
examines the importance of network security and digital forensics in the communication of
surveillance data as well as issues of issues of privacy and ethics discusses the Viola-Jones
object detection method and the HOG algorithm for pedestrian and human behavior recognition
reviews the use of artificial intelligence for automated monitoring of surveillance events and
decision-making approaches to determine the need for human intervention presents a case study
on a system that triggers an alarm when a vehicle fails to stop at a red light and identifies
the vehicle's license plate number investigates the use of cutting-edge supercomputing
technologies for digital surveillance such as FPGA GPU and parallel computing. This concise
and accessible work serves as a classroom-tested textbook for graduate-level courses on
intelligent surveillance. Researchers and engineers interested in entering this area will also
find the book suitable as a helpful self-study reference.
