Adaptive motion estimation using local measures of texture and similarity

Traditional approaches to the estimation of motion in video sequences have relied on the appropriate selection of various algorithm parameters. This dependence becomes a prohibitive drawback in applications where automation is desirable or necessary or in sequences where a single set of parameters can not achieve sufficiently accurate results. We investigate a number of techniques for locally adapting both the spatio-temporal filters and the hierarchical structure used in the estimation of optical flow. The surviving technique utilizes projected active contours and gradient-based Chamfer distance images to adapt the filters and a temporally-based Kolmogorov-Smirnov metric to locally adapt the hierarchical structure. The advantages of using these adaptive variations are demonstrated on articulated and self-occluding motion.