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Multi-linear algebra based techniques for foreground and background separation
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Tadimeti, Neha. Multi-linear algebra based techniques for foreground and background separation. Retrieved from https://doi.org/doi:10.7282/T3XD145W

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TitleMulti-linear algebra based techniques for foreground and background separation
NameTadimeti, Neha (author); Bajwa, Waheed U. (chair); Torres, Elizabeth (internal member); Gajic, Zoran (internal member); Patel, Vishal M. (internal member); Rutgers University; Graduate School - New Brunswick
Date Created2017
Other Date2017-01 (degree)
SubjectElectrical and Computer Engineering, Tensor algebra, Image processing
Extent1 online resource (vii, 26 p. : ill.)
DescriptionThe work presented in this thesis aims to understand the use of tensor algebra for background and foreground separation in videos. Specifically, it tries to explore the advantages of tensor-based approaches over the vector-based ones. In vector-based approaches, video frames are vectorized and concatenated into columns of a matrix for foreground and background separation. Through vectorization, one cannot explore the multi-dimensional aspect of video frames. Recent research has shown that tensor algebra can be helpful in extracting useful information from a multi-dimensional perspective. In this thesis, we propose two new algorithms which use tensor algebra to solve for background and foreground separation. In the first part of the thesis, we develop a mini-batch extension to Online Tensor Robust Principal Component Analysis (OTRPCA). The proposed extension signifi- cantly reduces the computational time in comparison to OTRPCA. It is also shown that the accuracy levels of background separation are higher than OTRPCA for a decent mini-batch size. As the mini-batch size further increases, accuracy levels fall as the dictionary update is one-shot and non-iterative. In the second part of the thesis, online vector-based Grassmanian Robust Adaptive Subspace Algorithm (GRASTA) is extended to tensor domain. The proposed Multi-Linear GRASTA (MLG) is also an online algorithm, thus suitable for real-time applications. Unlike the vector-based implementation, MLG explores the multi-dimensional nature of the video frames and solves for the separation problem across every dimension. MLG can process multiple frames at a time making it faster than other vector and tensor based separation algorithms. Detailed results are discussed which show that the accuracy of separation with MLG is competitive with the state-of-the-art.
NoteM.S.
NoteIncludes bibliographical references
Noteby Beha Tadimeti
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T3XD145W
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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