STDC‐Flow: large displacement flow field estimation using similarity transformation‐based dense correspondence

Abstract

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.