Foreground Detection via Robust Low Rank Matrix Decomposition Including Spatio-Temporal Constraint

@inproceedings{Guyon2012ForegroundDV,
  title={Foreground Detection via Robust Low Rank Matrix Decomposition Including Spatio-Temporal Constraint},
  author={Charles Guyon and Thierry Bouwmans and El-hadi Zahzah},
  booktitle={ACCV Workshops},
  year={2012}
}
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… CONTINUE READING
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