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 L 1 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.

Though the accuracy and robustness of optical flow has been dramatically enhanced over the past few years, the issue of edge-blurring near the image and motion boundaries has remained a challenge in flow field estimation. In this paper, we propose a refined total variation withL1norm (TV-L1) optical flow estimation approach using joint filtering, named JOF. First, we divide the image into three categorized regions: mutual-structure regions, inconsistent structure regions, and smooth regions. The mutual-structure guided filter for optical flow estimation is constructed by extracting the mutual-structure regions of the flow field. Second, the refined TV-L1optical flow model is proposed by incorporating the non-local term and mutual-structure guided filter objective function into the classical TV-L1energy function. Furthermore, the novel TV-L1optical flow objective function is minimized using a joint filtering program composed of a weighted median filter and a mutual-structure guided filter to optimize the estimated flow field during the coarse-to-fine optical flow computation scheme. Finally, we compare the proposed JOF method with several state-of-the-art approaches including variational and deep learning based optical flow models using the Middlebury, MPI-Sintel, and UCF101 test databases. The evaluation results indicate that the proposed method has high accuracy and good robustness for flow field computation and, especially, the significant benefit of edge-preserving.