Stochastic decomposition into low rank and sparse tensor for robust background subtraction

Sajid Javed, Thierry Bouwmans, Soon Ki Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

Background subtraction (BS) is a very important task for various computer vision applications. Higher-Order Robust Principal Component Analysis (HORPCA) based robust tensor recovery or decomposition provides a very nice potential for BS. The BG sequence is then modeled by underlying lowdimensional subspace called low-rank while the sparse tensor constitutes the foreground (FG) mask. However, traditional tensor based decomposition methods are sensitive to outliers and due to the batch optimization methods, high dimensional data should be processed. As a result, huge memory usage and computational issues arise in earlier approaches which are not desirable for real-time systems. In order to tackle these challenges, we apply the idea of stochastic optimization on tensor for robust low-rank and sparse error separation. Only one sample per time instance is processed from each unfolding matrices of tensor in our scheme to separate the low-rank and sparse component and update the low dimensional basis when a new sample is revealed. This iterative multi-dimensional tensor data optimization scheme for decomposition is independent of the number of samples and hence it reduces the memory and computational complexities. Experimental evaluations on both synthetic and real-world datasets demonstrate the robustness and comparative performance of our approach as compared to its batch counterpart without scarificing the online processing.

Original languageBritish English
Title of host publicationIET Seminar Digest
PublisherInstitution of Engineering and Technology
Edition5
ISBN (Print)9781785611315
DOIs
StatePublished - 2015
Event6th International Conference on Imaging for Crime Prevention and Detection, ICDP 2015 - London, United Kingdom
Duration: 15 Jul 201517 Jul 2015

Publication series

NameIET Seminar Digest
Number5
Volume2015

Conference

Conference6th International Conference on Imaging for Crime Prevention and Detection, ICDP 2015
Country/TerritoryUnited Kingdom
CityLondon
Period15/07/1517/07/15

Keywords

  • Background/Foreground Separation
  • Low-rank tensor
  • Stochastic optimization
  • Tensor decomposition

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