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Dense Motion Estimation for Smoke

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10114))

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Abstract

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.

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Acknowledgement

The authors are supported by the EPSRC project OAK EP/K02339X/1. We thank the reviewers for constructive comments, and also thank H. Gong for his helpful suggestion and comments.

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Authors and Affiliations

  1. Department of Computer Science, University of Bath, Bath, UK

    Da Chen & Peter Hall

  2. Department of Computer Science, University College London, London, UK

    Wenbin Li

Authors
  1. Da Chen
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  2. Wenbin Li
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  3. Peter Hall
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Corresponding author

Correspondence to Da Chen .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Chen, D., Li, W., Hall, P. (2017). Dense Motion Estimation for Smoke. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10114. Springer, Cham. https://doi.org/10.1007/978-3-319-54190-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-54190-7_14

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  • Online ISBN: 978-3-319-54190-7

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