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Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements

Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements

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 Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements

 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.


 


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  • Model: PROJ7253
  • 999 Units in Stock
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This product was added to our catalog on Monday 19 June, 2017.

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