Background Modeling from Video Sequences via Online Motion-Aware RPCA

Xu Weiyao1, Xia Ting1 and Jing Changqiang2

  1. Zaozhuang University
    Zaozhuang 277160, Shandong Province, China
    {xuweiyao 2008,xiayuxue121}
  2. Linyi University
    Linyi 276000, Shandong Province, China


Background modeling of video frame sequences is a prerequisite for computer vision applications. Robust principal component analysis(RPCA), which aims to recover low rank matrix in applications of data mining and machine learning, has shown improved background modeling performance. Unfortunately, The traditional RPCA method considers the batch recovery of low rank matrix of all samples, which leads to higher storage cost. This paper proposes a novel online motion-aware RPCA algorithm, named OM-RPCAT, which adopt truncated nuclear norm regularization as an approximation method for of low rank constraint. And then, Two methods are employed to obtain the motion estimation matrix, the optical flow and the frame selection, which are merged into the data items to separate the foreground and background. Finally, an efficient alternating optimization algorithm is designed in an online manner. Experimental evaluations of challenging sequences demonstrate promising results over state-of-the-art methods in online application.

Key words

Computer vision, Background modeling, Online RPCA, Truncated nu-clear norm

Digital Object Identifier (DOI)

Publication information

Volume 18, Issue 4 (September 2021)
Year of Publication: 2021
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Weiyao, X., Ting, X., Changqiang, J.: Background Modeling from Video Sequences via Online Motion-Aware RPCA. Computer Science and Information Systems, Vol. 18, No. 4, 1411–1426. (2021),