Optical flow from a least-trimmed squares based adaptive approach

Optical flow estimation can be formulated as two regression stages: derivative estimation and optical flow constraints (OFC) solving. Traditional approaches use least-squares at both stages and are sensitive to assumption violations. To improve estimation accuracy, especially near motion boundaries, we use a least trimmed squares (LTS) estimator to solve the OFC, obtaining a confidence measure for each estimate; and at place with low confidence, we use another LTS estimator to make the derivative estimation robust. This adaptive two-stage robust scheme has significantly higher accuracy than non-robust algorithms and those only using robust methods at the OFC stage. Advantages are illustrated on both synthetic and real data