Motion field discontinuity classification for tensor-based optical flow estimation

Abstract

A much more accurate classification scheme is proposed for structure tensor-based optical flow estimation to address the difficulties of interpreting motion field discontinuities. The key novelties of this approach are: (1) a scale-adaptive spatio-temporal filter; (2) a weighted structure tensor; (3) confidence measurements. Multiple motions of moving objects are matched by utilizing a spatio-temporal Gaussian filter with adaptive scale selection, which is steered by the condition number. To capture the neighborhood structure of local discontinuities, weighting the structure tensors is attempted. A new normalization function is exploited to facilitate accurate thresholding for confidence measurements. Experimental results demonstrate that these three novelties together effectively contribute much improved performance on motion field discontinuity classification compared with that of existing methods.