Effective hierarchical background modeling and foreground detection in surveillance systems

Background modeling is one of the most important parts of visual surveillance systems. Most background models are pixel-based which extract detailed shape of moving objects, but they are so sensitive to non-stationary scenes. In many applications there is no need to detect the detailed shape of moving objects. So some researchers use block-based methods instead of pixel-based which are more insensitive to local movements. These two methods are complementary to each other. We propose an efficient hierarchical method by which the block level information is utilized intelligently to improve the efficiency and robustness of pixel level. Experimental results demonstrate the effectiveness of the algorithm when applied in different outdoor and indoor environments.