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Background subtraction based on tensor nuclear and \(L_{1,1,2}\) norm

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Abstract

Background subtraction technology is a very important part in the field of video surveillance applications. The common matrix decomposition methods based on robust principal component analysis vectorized video sequences, which destroys the spatial structure and spatio-temporal continuity of videos. Aiming at this problem, a model based on tensor robust principal component analysis was proposed. In the new model, an improved tensor nuclear norm was used to constrain the background, which strengthened the low rank of background and improved the accuracy of foreground background separation. And \(L_{1,1,2}\) norm was applied to constrain foreground and enhanced the tube sparsity and spatio-temporal continuity of foreground, which improved the accuracy of foreground extraction. Experimental results show that the proposed algorithm outperforms multiple state-of-the-art methods both in qualitative and qualitative aspects, especially, for the videos with the multi-target or in bad weather.

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Acknowledgements

This project is partially supported by the National Natural Science Foundation of China (11961010, 61661017, 61967005, 61362021), Guangxi Natural Science Foundation (2018GXNSFAA138169, 2017GXNSFBA198212), Guangxi Colleges and Universities Key Laboratory project of Data Analysis and Computation (LDAC201704).

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Authors and Affiliations

  1. School of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, Guangxi, China

    Lixia Chen

  2. School of Architecture, Ranching Institute of Technology, Sanhe, Heibei, China

    Ying Ban

  3. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

    Xuewen Wang

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  1. Lixia Chen
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  2. Ying Ban
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  3. Xuewen Wang
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Correspondence to Lixia Chen.

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Chen, L., Ban, Y. & Wang, X. Background subtraction based on tensor nuclear and \(L_{1,1,2}\) norm. SIViP 16, 1053–1060 (2022). https://doi.org/10.1007/s11760-021-02054-6

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  • DOI: https://doi.org/10.1007/s11760-021-02054-6

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