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Optimal Dominant Motion Estimation using Adaptive Search of Transformation Space

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84037

Lampert,  CH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society;

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Zitation

Ulges, A., Lampert, C., Keysers, D., & Breuel, T. (2007). Optimal Dominant Motion Estimation using Adaptive Search of Transformation Space. Pattern Recognition: 29th DAGM Symposium, 204-215.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CBF3-9
Zusammenfassung
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.