Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences

Andrews Sobral, Sajid Javed, Soon Ki Jung, Thierry Bouwmans, El Hadi Zahzah

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

35 Scopus citations

Abstract

Background subtraction is an important task for visual surveillance systems. However, this task becomes more complex when the data size grows since the real-world scenario requires larger data to be processed in a more efficient way, and in some cases, in a continuous manner. Until now, most of background subtraction algorithms were designed for mono or trichromatic cameras within the visible spectrum or near infrared part. Recent advances in multispectral imaging technologies give the possibility to record multispectral videos for video surveillance applications. Due to the specific nature of these data, many of the bands within multispectral images are often strongly correlated. In addition, processing multispectral images with hundreds of bands can be computationally burdensome. In order to address these major difficulties of multispectral imaging for video surveillance, this paper propose an online stochastic framework for tensor decomposition of multispectral video sequences (OSTD). First, the experimental evaluations on synthetic generated data show the robustness of the OSTD with other state of the art approaches then, we apply the same idea on seven multispectral video bands to show that only RGB features are not sufficient to tackle color saturation, illumination variations and shadows problem, but the addition of six visible spectral bands together with one near infrared spectra provides a better background/foreground separation.

Original languageBritish English
Title of host publicationProceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages946-953
Number of pages8
ISBN (Electronic)9781467383905
DOIs
StatePublished - 11 Feb 2016
Event15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015 - Santiago, Chile
Duration: 11 Dec 201518 Dec 2015

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2015-February
ISSN (Print)1550-5499

Conference

Conference15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015
Country/TerritoryChile
CitySantiago
Period11/12/1518/12/15

Keywords

  • Matrix decomposition
  • Optimization
  • Robustness
  • Sparse matrices
  • Streaming media
  • Tensile stress
  • Video surveillance

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