A sequential detection framework for feature tracking within computational constraints

A unified decision-theoretic framework for automating the establishment of feature point correspondences in a temporally dense sequence of images is discussed. The approach extends a recent sequential detection algorithm to guide the detection and tracking of object feature points through an image sequence. The resulting extended feature tracks provide robust feature correspondences, for the estimation of three-dimensional structure and motion, over an extended number of image frames.<>