Foreground detection via robust low rank matrix decomposition including spatio-temporal constraint

  • Authors:

  • Charles Guyon;Thierry Bouwmans;El-Hadi Zahzah

  • Affiliations:

  • Laboratoire MIA (Mathematiques, Image et Applications), University of La Rochelle, France;Laboratoire MIA (Mathematiques, Image et Applications), University of La Rochelle, France;Laboratoire MIA (Mathematiques, Image et Applications), University of La Rochelle, France

  • Venue:
  • ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
  • Year:
  • 2012

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Abstract

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 and the temporal sparseness of moving objects (e.g. outliers). Experimental results on the BMC 2012 datasets show the pertinence of the proposed approach.