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Optimization of the Regularization in Background and Foreground Modeling
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Background and foreground modeling is a typical method in the application of computer vision.
The current general “low-rank + sparse” model decomposes the frames from the video sequences into low-rank background and sparse foreground.
But the sparse assumption in such a model may not conform with the reality, and the model cannot directly reflect the correlation between the background and foreground either.
Thus, we present a novel model to solve this problem by decomposing the arranged data matrix D into low-rank background L and moving foreground M.
Here, we only need to give the priori assumption of the background to be low-rank and let the foreground be separated from the background as much as possible.
Based on this division, we use a pair of dual norms, nuclear norm and spectral norm, to regularize the foreground and background, respectively.
Furthermore, we use a reweighted function instead of the normal norm so as to get a better and faster approximation model.
Detailed explanation based on linear algebra about our two models will be presented in this paper.
By the observation of the experimental results, we can see that our model can get better background modeling, and even simplified versions of our algorithms perform better than mainstream techniques IALM and GoDec.
American Psychological Association (APA)
Wang, Si-Qi& Feng, Xiang-Chu. 2014. Optimization of the Regularization in Background and Foreground Modeling. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-483465
Modern Language Association (MLA)
Wang, Si-Qi& Feng, Xiang-Chu. Optimization of the Regularization in Background and Foreground Modeling. Journal of Applied Mathematics No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-483465
American Medical Association (AMA)
Wang, Si-Qi& Feng, Xiang-Chu. Optimization of the Regularization in Background and Foreground Modeling. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-483465
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-483465