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Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Cao WF(曹文飞); Wang Y(王尧); Sun J(孙剑); Meng DY(孟德宇); Yang, Can; Cichocki, Andrzej; Xu ZB(徐宗本)
Source PublicationIEEE Transactions on Image Processing
Indexed BySCI ; EI
EI Accession number20163002639090
WOS IDWOS:000394573100001
Contribution Rank2
Funding OrganizationMajor State Basic Research Program under Grant 2013CB329404, in part by the National Natural Science Foundation of China under Grant 11501440, Grant 61273020, Grant 61373114, Grant 61472313, Grant 61501389, and Grant 61573270, in part by the Fundamental Research Funds for the Central Universities under Grant 1301030600, in part by the Hong Kong Research Grant Council under Grant 22302815, and in part by Hong Kong Baptist University under Gant FRG2/15-16/011.
AbstractBackground 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.
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS Research AreaComputer Science ; Engineering
Citation statistics
Cited Times:74[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorWang Y(王尧)
Affiliation1.School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China
2.School of Mathematics and Information Science, Shaanxi Normal University, Xi'an, 710119, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
4.Department of Mathematics, Hong Kong Baptist University, Hong Kong
5.RIKEN Brain Science Institute, Saitama, 351-0198, Japan
6.Systems Research Institute, Polish Academy of Sciences, Warsaw, 01-447, Poland
Recommended Citation
GB/T 7714
Cao WF,Wang Y,Sun J,et al. Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements[J]. IEEE Transactions on Image Processing,2016,25(9):4075-4090.
APA Cao WF.,Wang Y.,Sun J.,Meng DY.,Yang, Can.,...&Xu ZB.(2016).Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements.IEEE Transactions on Image Processing,25(9),4075-4090.
MLA Cao WF,et al."Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements".IEEE Transactions on Image Processing 25.9(2016):4075-4090.
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