A Multiscale Co-linearity Statistic Based Approach to Robust Background Modeling

Background subtraction is an essential task in several static camera based computer vision systems. Background modeling is often challenged by spatio-temporal changes occurring due to local motion and/or variations in illumination conditions. The background model is learned from an image sequence in a number of stages, viz. preprocessing, pixel/region feature extraction and statistical modeling of feature distribution. A number of algorithms, mainly focusing on feature extraction and statistical modeling have been proposed to handle the problems and comparatively little exploration has occurred at the preprocessing stage. Motivated by the fact that disturbances caused by local motions disappear at lower resolutions, we propose to represent the images at multiple scales in the preprocessing stage to learn a pyramid of background models at different resolutions. During operation, foreground pixels are detected first only at the lowest resolution, and only these pixels are further analy...

• Type: Conference
• Year: 2006
• Where: ACCV
• Authors: Prithwijit Guha, Dibyendu Palai, K. S. Venkatesh, Amitabha Mukerjee
• Keywords: ACCV 2006, Background Models, Computer Vision, Feature Extraction, Statistical Modeling