Real-time scene background initialization based on spatio-temporal neighborhood exploration
Abstract In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-t...
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Gespeichert in:
| Autor*in: |
Mseddi, Wided Souidene [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 78(2018), 6 vom: 09. Aug., Seite 7289-7319 |
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| Übergeordnetes Werk: |
volume:78 ; year:2018 ; number:6 ; day:09 ; month:08 ; pages:7289-7319 |
| Links: |
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| DOI / URN: |
10.1007/s11042-018-6399-1 |
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OLC2035060184 |
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| 520 | |a Abstract In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods. | ||
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| 700 | 1 | |a Attia, Rabah |4 aut | |
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10.1007/s11042-018-6399-1 doi (DE-627)OLC2035060184 (DE-He213)s11042-018-6399-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Mseddi, Wided Souidene verfasserin (orcid)0000-0002-3002-4033 aut Real-time scene background initialization based on spatio-temporal neighborhood exploration 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods. Background initialization Online clustering Spatial exploration Edge matching Jmal, Marwa aut Attia, Rabah aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 6 vom: 09. Aug., Seite 7289-7319 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:6 day:09 month:08 pages:7289-7319 https://doi.org/10.1007/s11042-018-6399-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 6 09 08 7289-7319 |
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10.1007/s11042-018-6399-1 doi (DE-627)OLC2035060184 (DE-He213)s11042-018-6399-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Mseddi, Wided Souidene verfasserin (orcid)0000-0002-3002-4033 aut Real-time scene background initialization based on spatio-temporal neighborhood exploration 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods. Background initialization Online clustering Spatial exploration Edge matching Jmal, Marwa aut Attia, Rabah aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 6 vom: 09. Aug., Seite 7289-7319 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:6 day:09 month:08 pages:7289-7319 https://doi.org/10.1007/s11042-018-6399-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 6 09 08 7289-7319 |
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Abstract In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Real-time scene background initialization based on spatio-temporal neighborhood exploration |
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Jmal, Marwa Attia, Rabah |
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