Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1049/iet-cvi.2019.0321 | IET COMPUTER VISION |
DocType | Volume | Issue |
Journal | 14 | 5 |
ISSN | Citations | PageRank |
1751-9632 | 0 | 0.34 |
References | Authors | |
0 | 6 | |