Security video surveillance systems, such as homeland security and national defence, rely on specific mathematical algorithms in order to run effectively. It is essential for these parameters to be understood in order to design and create a successful system. Video Surveillance Techniques and Technologies presents empirical research and acquired experience on the original solutions and mathematical algorithms for motion detection and object identification problems. Emphasising a wide variety of applications of security systems, this book is an essential tool for graduate students and professionals in the field of signal and image processing applied in static/moving object detection, tracking, and identification.

Moving object detection in video processing plays a very important role in many vision applications. The vision systems that include image processing methods are widely implemented in many areas as traffic control, video surveillance of unattended environments, etc. Most of the existing algorithms for moving object detection assume that the illumination in a scene remains constant. Unfortunately, this assumption is not valid, especially in outdoor environment. The efficiency of some of existing techniques diminishes significantly if the illumination varies. The problems associated with the background-based moving object detection techniques are mainly due to the variations of ambient lighting. Two effective moving object detection algorithms, based on the shading model method, are described. They showed to be robust under the conditions of extreme illumination variations unlike the other existing methods. The new methods are shown to be invariant to significant illumination changes and superior to other techniques when the illumination is allowed to vary.