Efficient and Robust Background Modeling with Dynamic Mode Decomposition
Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynam...
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| Autor*in: |
Krake, Tim [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of mathematical imaging and vision - Springer US, 1992, 64(2022), 4 vom: 24. Feb., Seite 364-378 |
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| Übergeordnetes Werk: |
volume:64 ; year:2022 ; number:4 ; day:24 ; month:02 ; pages:364-378 |
| Links: |
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| DOI / URN: |
10.1007/s10851-022-01068-0 |
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OLC2078509205 |
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| 520 | |a Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use. | ||
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10.1007/s10851-022-01068-0 doi (DE-627)OLC2078509205 (DE-He213)s10851-022-01068-0-p DE-627 ger DE-627 rakwb eng 510 VZ 31.00 bkl 54.00 bkl Krake, Tim verfasserin aut Efficient and Robust Background Modeling with Dynamic Mode Decomposition 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use. Dynamic mode decomposition Spectral decomposition Background modeling Foreground detection Bruhn, Andrés aut Eberhardt, Bernhard aut Weiskopf, Daniel aut Enthalten in Journal of mathematical imaging and vision Springer US, 1992 64(2022), 4 vom: 24. Feb., Seite 364-378 (DE-627)13114278X (DE-600)1127403-7 (DE-576)038688964 0924-9907 nnns volume:64 year:2022 number:4 day:24 month:02 pages:364-378 https://doi.org/10.1007/s10851-022-01068-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT 31.00 VZ 54.00 VZ AR 64 2022 4 24 02 364-378 |
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10.1007/s10851-022-01068-0 doi (DE-627)OLC2078509205 (DE-He213)s10851-022-01068-0-p DE-627 ger DE-627 rakwb eng 510 VZ 31.00 bkl 54.00 bkl Krake, Tim verfasserin aut Efficient and Robust Background Modeling with Dynamic Mode Decomposition 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use. Dynamic mode decomposition Spectral decomposition Background modeling Foreground detection Bruhn, Andrés aut Eberhardt, Bernhard aut Weiskopf, Daniel aut Enthalten in Journal of mathematical imaging and vision Springer US, 1992 64(2022), 4 vom: 24. Feb., Seite 364-378 (DE-627)13114278X (DE-600)1127403-7 (DE-576)038688964 0924-9907 nnns volume:64 year:2022 number:4 day:24 month:02 pages:364-378 https://doi.org/10.1007/s10851-022-01068-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT 31.00 VZ 54.00 VZ AR 64 2022 4 24 02 364-378 |
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10.1007/s10851-022-01068-0 doi (DE-627)OLC2078509205 (DE-He213)s10851-022-01068-0-p DE-627 ger DE-627 rakwb eng 510 VZ 31.00 bkl 54.00 bkl Krake, Tim verfasserin aut Efficient and Robust Background Modeling with Dynamic Mode Decomposition 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use. Dynamic mode decomposition Spectral decomposition Background modeling Foreground detection Bruhn, Andrés aut Eberhardt, Bernhard aut Weiskopf, Daniel aut Enthalten in Journal of mathematical imaging and vision Springer US, 1992 64(2022), 4 vom: 24. Feb., Seite 364-378 (DE-627)13114278X (DE-600)1127403-7 (DE-576)038688964 0924-9907 nnns volume:64 year:2022 number:4 day:24 month:02 pages:364-378 https://doi.org/10.1007/s10851-022-01068-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT 31.00 VZ 54.00 VZ AR 64 2022 4 24 02 364-378 |
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10.1007/s10851-022-01068-0 doi (DE-627)OLC2078509205 (DE-He213)s10851-022-01068-0-p DE-627 ger DE-627 rakwb eng 510 VZ 31.00 bkl 54.00 bkl Krake, Tim verfasserin aut Efficient and Robust Background Modeling with Dynamic Mode Decomposition 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use. Dynamic mode decomposition Spectral decomposition Background modeling Foreground detection Bruhn, Andrés aut Eberhardt, Bernhard aut Weiskopf, Daniel aut Enthalten in Journal of mathematical imaging and vision Springer US, 1992 64(2022), 4 vom: 24. Feb., Seite 364-378 (DE-627)13114278X (DE-600)1127403-7 (DE-576)038688964 0924-9907 nnns volume:64 year:2022 number:4 day:24 month:02 pages:364-378 https://doi.org/10.1007/s10851-022-01068-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT 31.00 VZ 54.00 VZ AR 64 2022 4 24 02 364-378 |
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Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use. © The Author(s) 2022 |
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Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use. © The Author(s) 2022 |
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Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use. © The Author(s) 2022 |
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2024-07-03T20:47:27.449Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2078509205</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506010936.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10851-022-01068-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2078509205</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10851-022-01068-0-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Krake, Tim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Efficient and Robust Background Modeling with Dynamic Mode Decomposition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. 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