Base selection in estimating sparse foreground in video

We investigate effective means of building robust dictionaries for detecting the sparse foreground in videos with static background. This work is an extension to our existing solution to foreground/background segmentation problem using the linear programming method proposed to detect sparse errors in signals, which are created by a known dictionary. The dictionary building methods we study are established robust component analysis techniques in the literature (i.e. k-SVD & robust-PCA) as well as a heuristic (running median) inspired by the highly correlated nature of the static video background signal. We compare the effectiveness of the new methods with our original system as well as a baseline method, which is the commonly used single Gaussian model of the background pixels.