Fast Local and Global Projection-Based Methods for Affine Motion Estimation

The demand for more effective compression, storage, and transmission of video data is ever increasing. To make the most effective use of bandwidth and memory, motion-compensated methods rely heavily on fast and accurate motion estimation from image sequences to compress not the full complement of frames, but rather a sequence of reference frames, along with “differences” between these frames which results from estimated frame-to-frame motion. Motivated by the need for fast and accurate motion estimation for compression, storage, and transmission of video as well as other applications of motion estimation, we present algorithms for estimating affine motion from video image sequences. Our methods utilize properties of the Radon transform to estimate image motion in a multiscale framework to achieve very accurate results. We develop statistical and computational models that motivate the use of such methods, and demonstrate that it is possible to improve the computational burden of motion estimation by more than an order of magnitude, while maintaining the degree of accuracy afforded by the more direct, and less efficient, 2-D methods.

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