Motion Interpretation using Adaptive Search of Transformation Space

Adrian Ulges

Department of Computer Science, Technical University Kaiserslautern IUPR Technical Reports 1 6/2007.


This report addresses the extraction of a parametric global motion from a motion field, 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 to generate ground truth for benchmarking motion estimation. Our main contributions are: first, the novel combination of a state- of-the-art quality 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 a basic model with an additional smoothness prior.


2007-IUPR-05Jun_2042.pdf (pdf, 3 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence