@inproceedings{4747,
  title = {Optimal Dominant Motion Estimation using Adaptive Search of Transformation Space},
  journal = {Pattern Recognition: 29th DAGM Symposium},
  booktitle = {DAGM 2007},
  abstract = {The extraction of a parametric global motion from a motion field is a task with several applications in video processing. We present two probabilistic formulations of the problem and carry out optimization using the RAST algorithm, a geometric matching method novel to motion estimation in video. RAST uses an exhaustive and adaptive search of transformation space and thus gives -- in contrast to local sampling optimization techniques used in the past -- a globally optimal solution. Among other applications, our framework can thus be used as a source of ground truth for benchmarking motion estimation algorithms. Our main contributions are: first, the novel combination of a state-of- the-art MAP criterion for dominant motion estimation with a search procedure that guarantees global optimality. Second, experimental re- sults that illustrate the superior performance of our approach on synthetic flow fields as well as real-world video streams. Third, a significant speedup of the search achieved by extending the mod
  el with an additional smoothness prior.},
  pages = {204-215},
  editors = {Hamprecht, F. A., C. Schn{\"o}rr, B. J{\"a}hne},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  institution = {German Association for Pattern Recognition},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2007},
  slug = {4747},
  author = {Ulges, A. and Lampert, CH. and Keysers, D. and Breuel, TM.},
  month_numeric = {9}
}