Abstract
The core step of video stabilization is to estimate global motion from locally extracted motion clues. Outlier motion clues are generated from moving objects in image sequence, which cause incorrect global motion estimates. Random Sample Consensus (RANSAC) is popularly used to solve such outlier problem. RANSAC needs to tune parameters with respect to the given motion clues, so it sometimes fail when outlier clues are increased than before. Adaptive RANSAC is proposed to solve this problem, which is based on Maximum Likelihood Sample Consensus (MLESAC). It estimates the ratio of outliers through expectation maximization (EM), which entails the necessary number of iteration for each frame. The adaptation sustains high accuracy in varying ratio of outliers and faster than RANSAC when fewer iteration is enough. Performance of adaptive RANSAC is verified in experiments using four images sequences.
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