Issue No. 08 - August (2011 vol. 33)
Anita Sellent , TU Braunschweig, Braunschweig
Daniel Cremers , TU Munich, Munich
Martin Eisemann , TU Braunschweig, Braunschweig
Marcus Magnor , TU Braunschweig, Braunschweig
Bastian Goldlücke , TU Munich, Munich
Traditional optical flow algorithms rely on consecutive short-exposed images. In this work, we make use of an additional long-exposed image for motion field estimation. Long-exposed images integrate motion information directly in the form of motion-blur. With this additional information, more robust and accurate motion fields can be estimated. In addition, the moment of occlusion can be determined. Considering the basic signal-theoretical problem in motion field estimation, we exploit the fact that long-exposed images integrate motion information to prevent temporal aliasing. A suitable image formation model relates the long-exposed image to preceding and succeeding short-exposed images in terms of dense 2D motion and per-pixel occlusion/disocclusion timings. Based on our image formation model, we describe a practical variational algorithm to estimate the motion field not only for visible image regions but also for regions getting occluded. Results for synthetic as well as real-world scenes demonstrate the validity of the approach.
Motion field estimation, motion blur, optical flow, occlusion, computational video.
Anita Sellent, Daniel Cremers, Martin Eisemann, Marcus Magnor, Bastian Goldlücke, "Motion Field Estimation from Alternate Exposure Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1577-1589, August 2011, doi:10.1109/TPAMI.2010.218