Robust optical flow computation based on least-median-of-squares regression
An optical flow estimation technique is presented which is based on the least-median-of-squares (LMedS) robust regression algorithm enabling more accurate flow estimates to be computed in the vicinity of motion discontinuities. The how is computed in a blockwise fashion using an affine model. Through the use of overlapping blocks coupled with a block shifting strategy, redundancy is introduced into the computation of the flow. This eliminates blocking effects common in most other techniques based on blockwise processing and also allows flow to be accurately computed in regions containing three distinct motions. A multiresolution Version of the technique is also presented, again based on LMedS regression, which enables image sequences containing large motions to be effectively handled. An extensive set of quantitative comparisons with a wide range of previously published methods are carried out using synthetic, realistic (computer generated images of natural scenes with known flow) and natural images. Both angular and absolute flow errors are calculated for those sequences with known optical how. Displaced frame difference error, used extensively in video compression, is used for those natural scenes with unknown flow. In all of the sequences tested, a comparison with those methods that result in a dense flow field (greater than 80% spatial coverage), show that the LMedS technique produces the least error irrespective of the error measure used.
optical flow estimation least-median-or-squares robust regression affine motion model motion discontinuity detection image sequence analysis