Tensor LearningUsing N-mode SVD for Dynamic Background Modelling and Subtraction

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 2nd Russian-Pacific Conference on Computer Technology and Applications
PublisherIEEE
Pages6-10
ISBN (Electronic)978-1-5386-3592-6
ISBN (Print)978-1-5386-1206-4
Publication statusPublished - Sep 2017

Conference

Title2nd Russia and Pacific Conference on Computer Technology and Applications (RPC)
PlaceRussian Federation
CityVladivostok
Period25 - 29 September 2017

Abstract

Background modelling and subtraction is an essential component in motion analysis with wide range of applications in computer vision, whereas the task becomes more challenging in context of complex scenarios such as dynamic backgrounds. In this paper, we address the problem of modelling dynamic backgrounds in online tensor leaning framework. We use Tucker decomposition to model thespatio-temporal correlation of video background. To facilitate the online execution of foreground detection, we incrementally update the subspace factor matrices and core tensor by using the N-mode SVD. For the upcoming frame, the estimate of new basis matrix is updated, whereas the contents from last observation are removed. Similarity measure based on pixel values is carried out to produce the foreground mask. Visual analysis on video datasets has revealed that the proposed approach is well suited against dynamically varying backgrounds. Our quantitative results show that the proposed strategy is superior to state-of-the-art methods.

Research Area(s)

  • background subtraction, tensor learning, incremental n-mode SVD

Citation Format(s)

Tensor LearningUsing N-mode SVD for Dynamic Background Modelling and Subtraction. / Khan, Sheheryar; Xu, Guoxia; Yan, Hong.

Proceedings of the 2nd Russian-Pacific Conference on Computer Technology and Applications. IEEE, 2017. p. 6-10.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)