FOREGROUND ESTIMATION BASED ON ROBUST LINEAR REGRESSION MODEL

Background subtraction is a basic task for many computer vision applications, yet in dynamic scenes it is still a challenging problem. In this paper, we propose a new method to deal with this difficulty. Our approach is based on robust linear regression model and casts background subtraction as a outlier signal estimation problem. In our linear regression model, we explicitly model the error term as a combination of two components: foreground outlier and background noise. The foreground outlier is sparse and can be arbitrarily large in most cases, while the background noise is relatively small and dispersed. In order to reliably estimate the coefficients under the constraint of sparse foreground outlier, we propose a new objective function. Then we transform the function to fit our problem by only estimating the foreground outlier and give the solution method. Experimental results demonstrate the effectiveness of our method.