26 April 2018 Background recovery via motion-based robust principal component analysis with matrix factorization
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Background recovery is a key technique in video analysis, but it still suffers from many challenges, such as camouflage, lighting changes, and diverse types of image noise. Robust principal component analysis (RPCA), which aims to recover a low-rank matrix and a sparse matrix, is a general framework for background recovery. The nuclear norm is widely used as a convex surrogate for the rank function in RPCA, which requires computing the singular value decomposition (SVD), a task that is increasingly costly as matrix sizes and ranks increase. However, matrix factorization greatly reduces the dimension of the matrix for which the SVD must be computed. Motion information has been shown to improve low-rank matrix recovery in RPCA, but this method still finds it difficult to handle original video data sets because of its batch-mode formulation and implementation. Hence, in this paper, we propose a motion-assisted RPCA model with matrix factorization (FM-RPCA) for background recovery. Moreover, an efficient linear alternating direction method of multipliers with a matrix factorization (FL-ADM) algorithm is designed for solving the proposed FM-RPCA model. Experimental results illustrate that the method provides stable results and is more efficient than the current state-of-the-art algorithms.
© 2018 SPIE and IS&T
Peng Pan, Peng Pan, Yongli Wang, Yongli Wang, Mingyuan Zhou, Mingyuan Zhou, Zhipeng Sun, Zhipeng Sun, Guoping He, Guoping He, "Background recovery via motion-based robust principal component analysis with matrix factorization," Journal of Electronic Imaging 27(2), 023034 (26 April 2018). https://doi.org/10.1117/1.JEI.27.2.023034 . Submission: Received: 2 January 2018; Accepted: 29 March 2018
Received: 2 January 2018; Accepted: 29 March 2018; Published: 26 April 2018

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