Region-Based Statistical Background Modeling for Foreground Object Segmentation

This paper proposes a novel region-based scheme for dynamically modeling time-evolving statistics of video background, leading to an effective segmentation of foreground moving objects for a video surveillance system. In [1] statisticalbased video surveillance systems employ a Bayes decision rule for classifying foreground and background changes in individual pixels. Although principal feature representations significantly reduce the size of tables of statistics, pixel-wise maintenance remains a challenge due to the computations and memory requirement. The proposed region-based scheme, which is an extension of the above method, replaces pixelbased statistics by region-based statistics through introducing dynamic background region (or pixel) merging and splitting. Simulations have been performed to several outdoor and indoor image sequences, and results have shown a significant reduction of memory requirements for tables of statistics while maintaining relatively good quality in foregr...

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Kristof Op De Beeck, Irene Y. H. Gu, Liyuan Li, Mats Viberg, Bart De Moor
• Keywords: Foreground Object Segmentation, ICIP 2006, Image Processing