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

Content Provider	IET Digital Library
Author	Zhang, Congxuan Chen, Zhen Xiong, Fan Liu, Wen Li, Ming Ge, Liyue
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.
Starting Page	248
Ending Page	258
Page Count	11
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 5, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0321
Journal	IET Computer Vision
Publisher Date	2020-03-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Boolean Function Computed Flow Field Computer Vision And Image Processing Technique Deep Learning-based Optical Flow Model Dense Correspondence Displacement Flow Field Estimation Filtering Method in Signal Processing Image Filtering Image Fusion Image Sequence Integral Transforms KITTI Evaluation Database L1 Norm Optical Flow Model Learning in AI Median Filter Motion Estimation Motion Occlusions MPI-Sintel Database Nearest Neighbour Method Nearest-neighbour Field Neural Computing Technique Neural Nets Occlusion Information Optical Flow Computation Optical, Image And Video Signal Processing Optimisation Optimisation Technique Quadratic Pseudo-Boolean Optimisation Fusion Sensor Fusion Similarity Transformation Statistics STDC-Flow Method Transform Variational Learning-based Optical Flow Model Video Signal Processing Weighted Median Filtering Optimisation
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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