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Background Modeling Through Spatiotemporal Edge Feature and Color

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

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Abstract

In this paper, we propose a new spatiotemporal edge feature for background modeling that can extract spatial and temporal (motion) features by considering the background model and current information. Previous work on background modeling considers mainly the spatial domain, which misses key temporal information. In our proposal, we create spatiotemporal edge features by using current and past background information by identifying the amount of change from past to present. By finding these differences, we can accurately detect the movement of objects that is more robust to noise and illumination variations. Moreover, our proposed background-modeling technique adapts to background changes that occur over time through a dynamic model update strategy. Additionally, we are enhancing the spatiotemporal edge features with color to maintain the characteristics of each other. Finally, we evaluated our proposed method on the publicly available CDNET 2012 dataset and compared with state-of-the-art methods. Our extensive evaluation and analysis show that our method outperforms previous methods on this dataset.

Funded in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under grant No. 307425/2017-7.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, South Korea

    Byeongwoo Kim, Oksam Chae & Jaemyun Kim

  2. Institute of Computing, University of Campinas (UNICAMP), Campinas, Brazil

    Adín Ramírez Rivera

Authors
  1. Byeongwoo Kim
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  2. Adín Ramírez Rivera
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  3. Oksam Chae
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  4. Jaemyun Kim
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Corresponding author

Correspondence to Jaemyun Kim .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Kim, B., Ramírez Rivera, A., Chae, O., Kim, J. (2019). Background Modeling Through Spatiotemporal Edge Feature and Color. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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