Motion estimation based on time-sequentially sampled imagery

Determining the parameters of motion within a time-varying scene is an important problem in such fields as computer vision, motion compensated video coding, and tracking. Most motion estimation algorithms operate on image data that has been sampled in both space and time. However, very little work has been done to investigate the impact of the underlying sampling strategy on the motion estimation problem. The authors investigate motion estimation with time-sequentially sampled image data. They consider both centroid-displacement-based and Fourier-based approaches to motion estimation with this type of data. For comparision, they also examine the performance of these estimators with conventional, frame-instantaneously sampled data. The motion estimators are developed and evaluated in the context of the tracking problem. In particular, they present extensive numerical results showing the performance of the motion estimators in a simulated tracking environment within which the assumptions underlying the development of the estimators are violated. These results suggest empirical rules for choosing parameter values for the estimators