Online and Batch Supervised Background Estimation via L1 Regression
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We propose a surprisingly simple model for supervised video background estimation. Our model is based on ℓ_1 regression. As existing methods for ℓ_1 regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures.
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