Multilinear Algebra Based Techniques for Foreground and Background Separation
RUTGERS - THE STATE UNIV PISCATAWAY NJ PISCATAWAY United States
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The 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 significantly 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 de-cent mini-batch size. As the mini-batch size further increases, accuracy levels fall as the dictionary update is one-shot and non-iterative.