A New M-Estimator Approach for Global Motion Estimation.
ABSTRACT Global motion estimation (GME) is an extensively used tool in a variety of video processing applications. The major challenge in this field is the presence of large foreground objects. There is a wide variety of algorithms addressing this problem. The major shortcoming of these algorithms is inconsistent performance over several video sequences. In this paper, we propose a GME approach that is fully automatic, can successfully handle large foreground objects and provides consistent results over a range of different video sequences. The proposed method initially coarsely determines the foreground pixels by a clustering technique. The effect of remaining foreground pixels in the estimation process are then reduced by using a modified Lorentzian estimator. Experimental results prove the superiority and consistency of the proposed method compared to some recent approaches.
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ABSTRACT: Parametric motion estimation is an important task for various video processing applications, such as analysis, segmentation, and coding. The process for such an estimation has to satisfy three requirements. It has to be fast, accurate, and robust in the presence of arbitrarily moving foreground objects. We introduce a two-step simplification scheme, suitable for Monte-Carlo-based perspective motion model estimation. For complexity reduction, the Helmholtz tradeoff estimator as well as random sample consensus are enhanced with this scheme and applied on Kanade-Lucas-Tomasi features as well as on video stream macroblock motion vector fields. For the feature-based estimation, good trackable features are detected and tracked on raw video sequences. For the block-based approach, motion vector fields from encoded H.264/AVC video streams are used. Results indicate that the complexity of the whole estimation process can be reduced by a factor of up to 10000 compared to state-of-the-art methods without losing estimation precision.IEEE Transactions on Circuits and Systems for Video Technology 01/2013; 23(4):607-620. · 1.82 Impact Factor