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U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic Environments

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Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1249))

Abstract

Optical Flow is a popular method of computer vision for motion estimation. In this paper, we present a refined optical flow estimation method. Central to our approach is exploiting contour information as most of the motion lies on the edges. Further, we have formulated it as sparse to dense motion estimation. Proposed method has been evaluated on challenging real life image sequences of KITTI and Fish4Knowledge database. Results demonstrate that method performs well in case of low contrast, highly cluttered background, dynamic background, occlusion and illumination change.

We would like to thank the Ministry of Electronics and Information Technology Government of India and the Media Lab Asia for financial assistance to carry out research.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology, Delhi, India

    Shilpi Gupta & Brejesh Lall

  2. Indian Institute of Information Technology, Sri City, Andhra Pradesh, India

    Prerana Mukherjee

  3. Indian Institute of Technology, Jodhpur, India

    Santanu Chaudhury

Authors
  1. Shilpi Gupta
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  2. Prerana Mukherjee
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  3. Santanu Chaudhury
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  4. Brejesh Lall
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Corresponding author

Correspondence to Shilpi Gupta .

Editor information

Editors and Affiliations

  1. Department of Computational and Data Sciences, Indian Institute of Science Bangalore, Bangalore, India

    R. Venkatesh Babu

  2. Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, India

    Mahadeva Prasanna

  3. Computer Science & Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    Vinay P. Namboodiri

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Gupta, S., Mukherjee, P., Chaudhury, S., Lall, B. (2020). U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic Environments. In: Babu, R.V., Prasanna, M., Namboodiri, V.P. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2019. Communications in Computer and Information Science, vol 1249. Springer, Singapore. https://doi.org/10.1007/978-981-15-8697-2_18

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  • DOI: https://doi.org/10.1007/978-981-15-8697-2_18

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  • Print ISBN: 978-981-15-8696-5

  • Online ISBN: 978-981-15-8697-2

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