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

Abstract

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 analyzed at higher resolutions to obtain a precise silhouette of the entire foreground blob. Such a scheme is also found to yield a significant reduction in computation. The second contribution in this paper involves the use of the co-linearity statistic (introduced by Mester et al. for the purpose of illumination independent change detection in consecutive frames) as a pixel neighborhood feature by assuming a linear model with a signal modulation factor and additive noise. The use of co-linearity statistic as a feature has shown significant performance improvement over intensity or combined intensity-gradient features. Experimental results and performance comparisons (ROC curves) for the proposed approach with other algorithms show significant improvements for several test sequences.