Background Subtraction Based on Low-Rank and Structured Sparse Decomposition
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Background Subtraction Based on Low-Rank and Structured Sparse Decomposition
Abstract— Background Subtraction Based on Low-Rank and Structured Sparse Decomposition. Low rank and sparse representation based methods, which make few specific assumptions about the background, have recently attracted wide attention in background modeling. With these methods, moving objects in the scene are modeled < Final Year Projects 2016 >as pixel-wised sparse outliers. However, in many practical scenarios, the distributions of these moving parts are not truly pixel-wised sparse but structurally sparse. Meanwhile a robust analysis mechanism is required to handle background regions or foreground movements with varying scales. Based on these two observations, we first introduce a class of structured sparsity-inducing norms to model moving objects in videos. In our approach, we regard the observed sequence as being constituted of two terms.
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