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학술대회 Robust Video Stabilization to Outlier Motion using Adaptive RANSAC
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최성록, 김태민, 유원필
International Conference on Intelligent Robots and Systems (IROS) 2009, pp.1897-1902
09MC3200, u-Robot 인지인프라 기술개발(주관 : u-City 환경기반 하이브리드 u-로봇 서비스 시스템 기술개발), 유원필
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. © 2009 IEEE.
KSP 제안 키워드
High accuracy, Image sequence, Moving Object, Random sample consensus, Video Stabilization, expectation maximization, global motion, maximum likelihood