Motion Estimation with Adaptive Regularization and Neighborhood Dependent Constraint

Modern variational motion estimation techniques use total variation regularization along with the l1 norm in constant brightness data term. An algorithm based on such homogeneous regularization is unable to preserve sharp edges and leads to increased estimation errors. A better solution is to modify regularizer along strong intensity variations and occluded areas. In addition, using neighborhood information with data constraint can better identify correspondence between image pairs than using only a point wise data constraint. In this work, we present a novel motion estimation method that uses neighborhood dependent data constraint to better characterize local image structure. The method also uses structure adaptive regularization to handle occlusions. The proposed algorithm has been evaluated on Middlebury's benchmark image sequence dataset and compared to state-of-the-art algorithms. Experiments show that proposed method can give better performance under noisy conditions.