Base selection in estimating sparse foreground in video

Authors:
Mert Dikmen;Shen-Fu Tsai;Thomas S. Huang
Affiliations:
Beckman Institute and Coordinated Science Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana, Champaign;Beckman Institute and Coordinated Science Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana, Champaign;Beckman Institute and Coordinated Science Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana, Champaign
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

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 [1] to foreground/background segmentation problem using the linear programming method [2] 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 [3] & robust-PCA [4]) 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.