Total Variation and Rank-1 Constraint RPCA for Background Subtraction
Background subtraction (BS) in video sequences is a main research field, and the aim is to separate moving objects in the foreground from stationary background. Using the framework of schemes-based robust principal component analysis (RPCA), we propose a novel BS method employing the more refined pr...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8454775/ |
| _version_ | 1818950503579516928 |
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| author | Jize Xue Yongqiang Zhao Wenzhi Liao Jonathan Cheung-Wai Chan |
| author_facet | Jize Xue Yongqiang Zhao Wenzhi Liao Jonathan Cheung-Wai Chan |
| author_sort | Jize Xue |
| collection | DOAJ |
| description | Background subtraction (BS) in video sequences is a main research field, and the aim is to separate moving objects in the foreground from stationary background. Using the framework of schemes-based robust principal component analysis (RPCA), we propose a novel BS method employing the more refined prior representations for the static and dynamic components of the video sequences. Specifically, the rank-1 constraint is exploited to describe the strong low-rank property of background layer (temporal correlation of static component), and 3-D total variation measure and <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm are used to model the spatial-temporal smoothness of foreground layer and sparseness of noise (dynamic component). This method introduces rank-1, smooth, and sparse properties into the RPCA framework for BS task, and it is dubbed TR1-RPCA. In addition, an efficient algorithm based on the alternating direction method of multipliers is designed to solve the proposed BS model. Extensive experiments on simulated and real videos demonstrate the superiority of the proposed method. |
| first_indexed | 2024-12-20T09:19:38Z |
| format | Article |
| id | doaj.art-391a583d08ac4d48a12c4cfb1ac2e243 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T09:19:38Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-391a583d08ac4d48a12c4cfb1ac2e2432022-12-21T19:45:19ZengIEEEIEEE Access2169-35362018-01-016499554996610.1109/ACCESS.2018.28687318454775Total Variation and Rank-1 Constraint RPCA for Background SubtractionJize Xue0Yongqiang Zhao1https://orcid.org/0000-0002-6974-7327Wenzhi Liao2Jonathan Cheung-Wai Chan3School of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaDepartment of Telecommunications and Information Processing, Ghent University-TELIN-IMEC, Ghent, BelgiumDepartment of Electronics and Informatics, Vrije Universiteit Brussel, Brussels, BelgiumBackground subtraction (BS) in video sequences is a main research field, and the aim is to separate moving objects in the foreground from stationary background. Using the framework of schemes-based robust principal component analysis (RPCA), we propose a novel BS method employing the more refined prior representations for the static and dynamic components of the video sequences. Specifically, the rank-1 constraint is exploited to describe the strong low-rank property of background layer (temporal correlation of static component), and 3-D total variation measure and <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm are used to model the spatial-temporal smoothness of foreground layer and sparseness of noise (dynamic component). This method introduces rank-1, smooth, and sparse properties into the RPCA framework for BS task, and it is dubbed TR1-RPCA. In addition, an efficient algorithm based on the alternating direction method of multipliers is designed to solve the proposed BS model. Extensive experiments on simulated and real videos demonstrate the superiority of the proposed method.https://ieeexplore.ieee.org/document/8454775/Background subtractiontotal variationrank-1 propertyrobust principal component analysisspatial-temporal correlations |
| spellingShingle | Jize Xue Yongqiang Zhao Wenzhi Liao Jonathan Cheung-Wai Chan Total Variation and Rank-1 Constraint RPCA for Background Subtraction IEEE Access Background subtraction total variation rank-1 property robust principal component analysis spatial-temporal correlations |
| title | Total Variation and Rank-1 Constraint RPCA for Background Subtraction |
| title_full | Total Variation and Rank-1 Constraint RPCA for Background Subtraction |
| title_fullStr | Total Variation and Rank-1 Constraint RPCA for Background Subtraction |
| title_full_unstemmed | Total Variation and Rank-1 Constraint RPCA for Background Subtraction |
| title_short | Total Variation and Rank-1 Constraint RPCA for Background Subtraction |
| title_sort | total variation and rank 1 constraint rpca for background subtraction |
| topic | Background subtraction total variation rank-1 property robust principal component analysis spatial-temporal correlations |
| url | https://ieeexplore.ieee.org/document/8454775/ |
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