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International Conference on Intelligent Computing

ICIC 2015: Intelligent Computing Theories and Methodologies pp 400–411Cite as

Sparse Learning for Robust Background Subtraction of Video Sequences

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

Abstract

Sparse representation has been applied to background detecting by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based methods only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the foreground and the background. Learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. To take these challenges, this paper proposes a new method for robust background detecting via sparse representation. Our method explores both the strength of the well-patch adaptive dictionary learning technique to video frame structure analysis and the robustness background detection by the l 1 -norm data-fidelity term. By using linear sparse combinations of dictionary atom, the proposed method learns the sparse representations of video frame regions corresponding to candidate particles. The experiments show that the proposed method is able to tolerate the background clutter and video frame deterioration, and improves the existing detecting performance.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (No.61373109, No.61003127), State Key Laboratory of Software Engineering (SKLSE2012-09-31).

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Authors and Affiliations

  1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China

    Yuhan Luo & Hong Zhang

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China

    Yuhan Luo & Hong Zhang

Authors
  1. Yuhan Luo
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  2. Hong Zhang
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Corresponding author

Correspondence to Hong Zhang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Luo, Y., Zhang, H. (2015). Sparse Learning for Robust Background Subtraction of Video Sequences. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_39

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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