Hybrid Model-Based Estimation of Multiple Non-dominant Motions

The estimation of motion in videos yields information useful in the scope of video annotation, retrieval and compression. Current approaches use iterative minimization techniques based on intensity gradients in order to estimate the parameters of a 2D transform between successive frames. These approaches rely on good initial guesses of the motion parameters. For single or dominant motion there exist hybrid algorithms that estimate such initial parameters prior to the iterative minimization. We propose a technique for the generation of a set of motion hypotheses using blockmatching that also works in the presence of multiple non-dominant motions. These hypotheses are then refined using iterative techniques.