Robust Foreground Segmentation Using Subspace Based Background Model

Authors:
JiXiang Zhang;Yuan Tian;YiPing Yang;ChengFei Zhu
Affiliations:
-;-;-;-
Venue:
APCIP '09 Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02
Year:
2009

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Abstract

Robust foreground segmentation is an essential step in many computer vision applications such as visual surveillance and behavior analysis. This paper proposes a subspace based background modeling and foreground segmentation algorithm, which improves the incremental background subspace learning in a robust manner. It can efficiently reduce the influence of the foreground pixels which are undesired in background updating procedure, at the same time, adapts well to background variations. Furthermore, a novel subspace initialization method based on L1-minimization is proposed to efficiently construct the subspace background model using global information, without the requirement of empty scene. Experimental results demonstrate the robustness and effectiveness of the algorithm.