Motion Interpretation using Adaptive Search of Transformation Space
Adrian Ulges
Department of Computer Science, Technical University Kaiserslautern, IUPR Technical Reports, Vol. 1, 6/2007.
Abstract
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.
