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Robot Intelligence Technology and Applications 3 pp 685–691Cite as

Dense Optical Flow Estimation with 3D Structure Tensor Models

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 345))

Abstract

Since the 3D structure tensor at each pixel can interpret the local between frames well, it can be used to estimate dense flow. According to the assumptions of brightness constancy, the optical flow estimation can be converted to the calculation the eigenvector of the structure tensor, rather than the complex calculation of linear system. Iterative coarse-to-fine refinement is used to improve the performance. Experimental results show that the proposed algorithm is robust and effective for computing the dense flow.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China

    Tongwei Lu

  2. Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology), Wuhan, China

    Ying Ren, Wenting Liu & Anyuan Chen

Authors
  1. Tongwei Lu
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  2. Ying Ren
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  3. Wenting Liu
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  4. Anyuan Chen
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Corresponding author

Correspondence to Tongwei Lu .

Editor information

Editors and Affiliations

  1. Div. Electrical Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejeon, Korea, Republic of (South Korea)

    Jong-Hwan Kim

  2. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China

    Weimin Yang

  3. School of Information and Communication Technology, Griffith University, Gold Coast, Australia

    Jun Jo

  4. Center for Intelligent Technologies, Technical University of Kosice, Kosice, Slovakia

    Peter Sincak

  5. Civil and Environmental Engg., KAIST, Daejeon, Korea, Republic of (South Korea)

    Hyun Myung

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© 2015 Springer International Publishing Switzerland

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Lu, T., Ren, Y., Liu, W., Chen, A. (2015). Dense Optical Flow Estimation with 3D Structure Tensor Models. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_62

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  • DOI: https://doi.org/10.1007/978-3-319-16841-8_62

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16840-1

  • Online ISBN: 978-3-319-16841-8

  • eBook Packages: EngineeringEngineering (R0)

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