Robust Optical Flow in Rainy Scenes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-10-07

AUTHORS

Ruoteng Li , Robby T. Tan , Loong-Fah Cheong

ABSTRACT

Optical flow estimation in rainy scenes is challenging due to degradation caused by rain streaks and rain accumulation, where the latter refers to the poor visibility of remote scenes due to intense rainfall. To resolve the problem, we introduce a residue channel, a single channel (gray) image that is free from rain, and its colored version, a colored-residue image. We propose to utilize these two rain-free images in computing optical flow. To deal with the loss of contrast and the attendant sensitivity to noise, we decompose each of the input images into a piecewise-smooth structure layer and a high-frequency fine-detail texture layer. We combine the colored-residue images and structure layers in a unified objective function, so that the estimation of optical flow can be more robust. Results on both synthetic and real images show that our algorithm outperforms existing methods on different types of rain sequences. To our knowledge, this is the first optical flow method specifically dealing with rain. We also provide an optical flow dataset consisting of both synthetic and real rain images. More... »

PAGES

299-317

References to SciGraph publications

  • 2006. Bilateral Filtering-Based Optical Flow Estimation with Occlusion Detection in COMPUTER VISION – ECCV 2006
  • 2007-10. Vision and Rain in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2005-02. Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2016-12. DeepMatching: Hierarchical Deformable Dense Matching in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2007. Illumination-Robust Variational Optical Flow with Photometric Invariants in PATTERN RECOGNITION
  • 1994-02. Performance of optical flow techniques in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2011-03. A Database and Evaluation Methodology for Optical Flow in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. A Naturalistic Open Source Movie for Optical Flow Evaluation in COMPUTER VISION – ECCV 2012
  • 2009. An Improved Algorithm for TV-L 1 Optical Flow in STATISTICAL AND GEOMETRICAL APPROACHES TO VISUAL MOTION ANALYSIS
  • 2008. An Unbiased Second-Order Prior for High-Accuracy Motion Estimation in PATTERN RECOGNITION
  • Book

    TITLE

    Computer Vision – ECCV 2018

    ISBN

    978-3-030-01266-3
    978-3-030-01267-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-01267-0_18

    DOI

    http://dx.doi.org/10.1007/978-3-030-01267-0_18

    DIMENSIONS

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