STDC-Flow: large displacement flow field estimation using similarity transformation-based dense correspondence
In order to improve the accuracy and robustness of optical flow computation under large displacements and motion occlusions, the authors present in this study a large displacement flow field estimation approach using similarity transformation-based dense correspondence, named STDC-Flow approach. First, the authors compute an initial nearest-neighbour field by using the STDC-Flow of the consecutive two frames, and then extract the consistent regions as the robust nearest-neighbour field and label the inconsistent regions as the occlusion areas. Second, they improve a non-local total variation with the L1 norm optical flow model by using the occlusion information to modify the weighted median filtering optimisation. Third, they fuse the robust nearest-neighbour field and the computed flow field of the improved variational optical flow model to construct the final flow field by using the quadratic pseudo-boolean optimisation fusion algorithm. Finally, the authors compare the proposed STDC-Flow method with several state-of-the-art approaches including the variational and deep learning-based optical flow models by using the MPI-Sintel and KITTI evaluation databases. The comparison results demonstrate that the proposed STDC-Flow method has a high accuracy for flow field computation, especially the capacity of dealing with large displacements and motion occlusions.