A regularized tensor decomposition method with adaptive rank adjustment for Compressed-Sensed-Domain background subtraction

作者：

Highlights：

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• A regularized decomposition method with adaptive rank adjustment is proposed.

• A non-convex function as the surrogate of the ranks of video background tensor.

• An efficient algorithm developed based on the ADMM to solve the proposed model.

摘要

•A regularized decomposition method with adaptive rank adjustment is proposed.•A non-convex function as the surrogate of the ranks of video background tensor.•An efficient algorithm developed based on the ADMM to solve the proposed model.

论文关键词：Background subtraction,Compressive sensing,Adaptive rank adjustment,Non-convex surrogate,Alternative direction multiplier method

论文评审过程：Received 25 February 2017, Revised 28 December 2017, Accepted 29 December 2017, Available online 11 January 2018, Version of Record 2 February 2018.

论文官网地址：https://doi.org/10.1016/j.image.2017.12.011