Research

Robust Optical Flow Estimation for Continuous Blurred Scenes using RGB-Motion Imaging and Directional Filtering


Reference:

Cosker, D. and Li, W., 2013. Robust Optical Flow Estimation for Continuous Blurred Scenes using RGB-Motion Imaging and Directional Filtering. In: IEEE Winter Conference on Applications of Computer Vision, 2013-07-30.

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    Abstract

    Optical flow estimation is a difficult task given real-world video footage with camera and object blur. In this paper, we combine a 3D pose&position tracker with an RGB sensor allowing us to capture video footage together with 3D camera motion. We show that the additional camera motion information can be embedded into a hybrid optical flow framework by interleaving an iterative blind deconvolution and warping based minimization scheme. Such a hybrid framework significantly improves the accuracy of optical flow estimation in scenes with strong blur. Our approach yields improved overall performance against three state-of-the-art baseline methods applied to our proposed ground truth sequences as well as in several other real-world cases.

    Details

    Item Type Conference or Workshop Items (Paper)
    CreatorsCosker, D.and Li, W.
    DepartmentsFaculty of Science > Computer Science
    Research CentresMedia Technology Research Centre
    Publisher StatementmoBlur_WACV_v1.1.pdf: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    RefereedYes
    StatusPublished
    ID Code45561

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