关键词: 鲁棒主成分分析（RPCA）, 低秩建模, 空间约束, 前景检测

Abstract: Low rank and sparse decomposition based methods use [l1]-norm penalty to model moving object as sparse outliers. With these methods, the observed video matrix can be decomposed into a sparse matrix and a low-rank matrix, which model the foreground and background respectively. However, in many practical scenarios, the background is usually affected by dynamic background（e.g.water surface, waving trees）, thus the fixed [l1]-norm cannot offer a satisfactory performance. The distributions of the moving parts are not only pixel-wised sparse but also spatial continuous. In this paper, by introducing the spatial fused sparse constraint into low-rank model, the foreground is constrained by both spatial continuity constraint and sparse constraint. Furthermore, an adaptive parameter selection method is adopted in the proposed method. Experiments on public datasets demonstrate that the proposed method outperforms the state-of-the-arts methods and works effectively on complex videos.

Key words: Robust Principle Component Analysis（RPCA）, low-rank modeling, spatial constraint, foreground detection