Abstract / Description of output
Large motions remain a challenge for current optical flow algorithms. Traditionally, large motions are addressed using multi-resolution representations like Gaussian pyramids. To deal with large displacements, many pyramid levels are needed and, if an object is small, it may be invisible at the highest levels. To address this we decompose images using a channel representation (CR) and replace the standard brightness constancy assumption with a descriptor constancy assumption. CRs can be seen as an over-segmentation of the scene into layers based on some image feature. If the appearance of a foreground object differs from the background then its descriptor will be different and they will be represented in different layers. We create a pyramid by smoothing these layers, without mixing foreground and background or losing small objects. Our method estimates more accurate flow than the baseline on the MPI-Sintel benchmark, especially for fast motions and near motion boundaries.
Original language | English |
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Title of host publication | Computer Vision - ECCV 2014 |
Editors | David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars |
Place of Publication | Cham |
Publisher | Springer |
Pages | 423-438 |
Number of pages | 16 |
ISBN (Print) | 978-3-319-10590-1 |
Publication status | Published - 2014 |
Event | European Conference on Computer Vision 2014 - Zurich, Switzerland Duration: 5 Sept 2014 → 12 Sept 2014 |
Conference
Conference | European Conference on Computer Vision 2014 |
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Abbreviated title | ECCV 2014 |
Country/Territory | Switzerland |
City | Zurich |
Period | 5/09/14 → 12/09/14 |