Automated Multi-Camera Surveillance: Algorithms and Practice


Series: The International Series in Video Computing, Vol. 10 Javed, Omar and Shah, Mubarak 2008, X, 110 p. 47 illus., Hardcover ISBN: 978-0-387-78880-7

The development of surveillance systems has captured the interest of both the research and the industrial worlds in recent years. The aim of this effort is to increase security and safety in several application domains such as national security, home and bank safety, traffic monitoring and navigation, tourism, and military applications. The video surveillance systems currently in use share one feature: A human operator must monitor them at all times, thus limiting the number of cameras and the area under surveillance and increasing cost. A more advantageous system would have continuous active warning capabilities, able to alert security officials during or even before the happening of a crime.

Existing automated surveillance systems can be classified into categories according to:
The environment they are are primarily designed to observe;
The number of sensors that the automated surveillance system can handle;
The mobility of sensor.

The primary concern of this book is surveillance in an outdoor urban setting, where it is not possible for a single camera to observe the complete area of interest. Multiple cameras are required to observe such large environments. This book discusses and proposes techniques for development of an automated multi-camera surveillance system for outdoor environments, while identifying the important issues that a system needs to cope with in realistic surveillance scenarios. The goal of the research presented in this book is to build systems that can deal effectively with these realistic surveillance needs.

Written for:
Researchers and graduate students working in the fields of computer vision, image processing, artificial intelligence and pattern recognition; industry practitioners specializing in automated surveillance and other applications of computer vision

Keywords:
Automated Surveillance
Computer Vision
Image Processing
KNIGHT
Object detection
Visual Surveillance
multi-sensor tracking
single-sensor tracking