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Case-based background modeling: associative background database towards low-cost and high-performance change detection

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Abstract

Background modeling and subtraction is an essential task in video surveillance applications. Many researchers have discussed about an improvement of performance of a background model, and a reduction of memory usage or computational cost. To adapt to background changes, a background model has been enhanced by introducing various information including a spatial consistency, a temporal tendency, etc. with a large memory allocation. Meanwhile, an approach to reduce a memory cost cannot provide better accuracy of a background subtraction. To tackle the trade-off problem, this paper proposes a novel framework named “case-based background modeling”. The characteristics of the proposed method are (1) a background model is created, or removed when necessary, (2) case-by-case model sharing by some of the pixels, (3) pixel features are divided into two groups, one for model selection and the other for modeling. These approaches realize a low-cost and high accurate background model. The memory usage and the computational cost could be reduced by half of a traditional method and the accuracy was superior to the method.

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Notes

  1. Ground truth available at http://limu.ait.kyushu-u.ac.jp/dataset/.

  2. The T2FGMM_UM [5] is excluded from the comparison since we evaluated the performance of T2FGMM_UM with BGSLibrary available at http://code.google.com/p/bgslibrary/.

  3. The implementation of GMM-based background modeling (baseline) was achieved by the same programmer, and the Adaptive GMM and case-based GMM (proposed method) were based on the modification of the GMM-based method.

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Authors and Affiliations

  1. Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan

    Atsushi Shimada, Yosuke Nonaka, Hajime Nagahara & Rin-ichiro Taniguchi

Authors
  1. Atsushi Shimada
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  2. Yosuke Nonaka
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  3. Hajime Nagahara
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  4. Rin-ichiro Taniguchi
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Correspondence to Atsushi Shimada.

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Shimada, A., Nonaka, Y., Nagahara, H. et al. Case-based background modeling: associative background database towards low-cost and high-performance change detection. Machine Vision and Applications 25, 1121–1131 (2014). https://doi.org/10.1007/s00138-013-0563-4

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  • DOI: https://doi.org/10.1007/s00138-013-0563-4

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