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Background Subtraction: Model-Sharing Strategy Based on Temporal Variation Analysis

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Abstract

This paper presents a new approach for moving detection in complex scenes. Different with previous methods which compare a pixel with its own model and make the model more complex, we take an iterative model-sharing strategy as the process of foreground decision. The current pixel is not only compared with its own model, but may also compared with other pixel’s model which has similar temporal variation. Experiments show that the proposed approach leads to a lower false positive rate and higher precision. It has a better performance when compared with traditional approach.

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Authors and Affiliations

  1. Beijing Institute of Technology, Zhongguancun Road, Haidian District, Beijing, People’s Republic of China

    Yufeng Chen, Kun Zhao, Wenzhe Wu & Shikai Liu

Authors
  1. Yufeng Chen
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  2. Kun Zhao
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  3. Wenzhe Wu
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  4. Shikai Liu
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Corresponding author

Correspondence to Kun Zhao .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Chen, Y., Zhao, K., Wu, W., Liu, S. (2015). Background Subtraction: Model-Sharing Strategy Based on Temporal Variation Analysis. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_25

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  • DOI: https://doi.org/10.1007/978-3-319-16631-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16630-8

  • Online ISBN: 978-3-319-16631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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