Foreground detection via robust low rank matrix factorization including spatial constraint with Iterative reweighted regression

Foreground detection is the first step in video surveillance system to detect moving objects. Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change ov...

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Bibliographic Details
Main Authors: Guyon, C., Bouwmans, T., Zahzah, E.
Format: Conference Proceeding
Language:English
Subjects:
Matrix decomposition
Minimization
Noise
Optimized production technology
Principal component analysis
Robustness
Sparse matrices
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Summary:Foreground detection is the first step in video surveillance system to detect moving objects. Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. In this paper, we propose to use a low-rank matrix factorization with IRLS scheme (Iteratively reweighted least squares) and to address in the minimization process the spatial connexity of the pixels. Experimental results on the Wallflower and I2R datasets show the pertinence of the proposed approach.
ISSN:1051-4651
2831-7475