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

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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.
Original languageEnglish
Publication statusPublished - 30 Jan 2013
EventIEEE Winter Conference on Applications of Computer Vision - , UK United Kingdom
Duration: 30 Jul 2013 → …

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision
CountryUK United Kingdom
Period30/07/13 → …

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Cite this

Cosker, D., & Li, W. (2013). Robust Optical Flow Estimation for Continuous Blurred Scenes using RGB-Motion Imaging and Directional Filtering. Paper presented at IEEE Winter Conference on Applications of Computer Vision, UK United Kingdom.