ABSTRACT
Accurate and real-time video surveillance techniques for removing background variations in a video stream, which are highly correlated between frames, are at the forefront of modern data-analysis research. The objective in such algorithms is to highlight foreground objects of potential interest. Background/foreground separation is typically an integral step in detecting, identifying, tracking, and recognizing objects in video sequences. Most modern computer vision applications demand algorithms that can be implemented in real-time, and that are robust enough to handle diverse, complicated, and cluttered backgrounds. Competitive methods often need to be flexible enough to accommodate changes in a scene due to, for instance, illumination changes that can occur throughout the day, or location changes where the application is being implemented. Given the importance of this task, a variety of iterative techniques and methods have already been developed in order to perform background/- foreground separation [4, 8, 11, 15, 23, 24] (See also, for instance, the recent reviews by Bouwmans [2] and Benezeth et al. [1], which compare error and timing of various methods).
