Guided Sampling and Consensus for Motion Estimation

We present techniques for improving the speed of robust motion estimation based on random sampling of image features. Starting from Torr and Zisserman’s MLESAC algorithm, we address some of the problems posed from both practical and theoretical standpoints and in doing so allow the random search to be replaced by a guided search. Guidance of the search is based on readily-available information which is usually discarded, but can significantly reduce the search time. This guided-sampling algorithm is further specialised for tracking of multiple motions, for which results are presented.