Monte-Carlo-Based Parametric Motion Estimation Using a Hybrid Model Approach

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.