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Incremental and Multi-feature Tensor Subspace Learning Applied for Background Modeling and Subtraction

  • Andrews SobralEmail author
  • Christopher G. Baker
  • Thierry Bouwmans
  • El-hadi Zahzah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)

Abstract

Background subtraction (BS) is the art of separating moving objects from their background. The Background Modeling (BM) is one of the main steps of the BS process. Several subspace learning (SL) algorithms based on matrix and tensor tools have been used to perform the BM of the scenes. However, several SL algorithms work on a batch process increasing memory consumption when data size is very large. Moreover, these algorithms are not suitable for streaming data when the full size of the data is unknown. In this work, we propose an incremental tensor subspace learning that uses only a small part of the entire data and updates the low-rank model incrementally when new data arrive. In addition, the multi-feature model allows us to build a robust low-rank background model of the scene. Experimental results shows that the proposed method achieves interesting results for background subtraction task.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andrews Sobral
    • 1
    Email author
  • Christopher G. Baker
    • 3
  • Thierry Bouwmans
    • 2
  • El-hadi Zahzah
    • 1
  1. 1.Laboratoire L3IUniversité de La RochelleLa RochelleFrance
  2. 2.Laboratoire MIAUniversité de La RochelleLa RochelleFrance
  3. 3.Computer Sciences CorporationFalls ChurchUSA

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