Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/180475
Title: Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements
Authors: Wenfei Cao;Yao Wang;Jian Sun;Deyu Meng;Can Yang;Andrzej Cichocki;Zongben Xu
subject: tensor robust principal component analysis|Background subtraction|nonlocal self-similarity|video surveillance|Tucker tensor decomposition|compressive imaging|3D total variation
Year: 2016
Publisher: IEEE
Abstract: Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing, and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust principal component analysis (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model. To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using the alternating direction method of multipliers are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.
URI: http://localhost/handle/Hannan/180475
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
More Information: 4075
4090
Appears in Collections:2016

Files in This Item:
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Title: Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements
Authors: Wenfei Cao;Yao Wang;Jian Sun;Deyu Meng;Can Yang;Andrzej Cichocki;Zongben Xu
subject: tensor robust principal component analysis|Background subtraction|nonlocal self-similarity|video surveillance|Tucker tensor decomposition|compressive imaging|3D total variation
Year: 2016
Publisher: IEEE
Abstract: Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing, and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust principal component analysis (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model. To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using the alternating direction method of multipliers are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.
URI: http://localhost/handle/Hannan/180475
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
More Information: 4075
4090
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7488247.pdf7.81 MBAdobe PDFThumbnail
Preview File
Title: Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements
Authors: Wenfei Cao;Yao Wang;Jian Sun;Deyu Meng;Can Yang;Andrzej Cichocki;Zongben Xu
subject: tensor robust principal component analysis|Background subtraction|nonlocal self-similarity|video surveillance|Tucker tensor decomposition|compressive imaging|3D total variation
Year: 2016
Publisher: IEEE
Abstract: Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing, and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust principal component analysis (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model. To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using the alternating direction method of multipliers are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.
URI: http://localhost/handle/Hannan/180475
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
More Information: 4075
4090
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7488247.pdf7.81 MBAdobe PDFThumbnail
Preview File