Publication
Optimal Dominant Motion Estimation using Adaptive Search of Transformation Space
Adrian Ulges; Christoph Lampert; Daniel Keysers; Thomas Breuel
In: Pattern Recognition, 29th Annual DAGM Symposium. Annual Symposium of the German Association for Pattern Recognition (DAGM-07), September 12-14, Heidelberg, Germany, Pages 204-213, Lecture Notes in Computer Science (LNCS), Vol. 4713, ISBN 978-3-540-74933-2, Springer, 9/2007.
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 results 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 model with an additional smoothness prior.