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Optimal Dominant Motion Estimation Using Adaptive Search of Transformation Space

  • Adrian Ulges
  • Christoph H. Lampert
  • Daniel Keysers
  • Thomas M. Breuel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)

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.

Keywords

Motion Vector Motion Estimation Spatial Coherence Global Motion Foreground Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Adrian Ulges
    • 1
  • Christoph H. Lampert
    • 2
  • Daniel Keysers
    • 3
  • Thomas M. Breuel
    • 1
  1. 1.Department of Computer Science, Technical University of Kaiserslautern 
  2. 2.Department for Empirical Inference, Max-Planck-Institute for Biological Cybernetics, Tübingen 
  3. 3.Image Understanding and Pattern Recognition Group, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern 

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