Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry
The variational optical flow model is used in this work to investigate a subgrid-scale optimization approach for modeling complex fluid flows in image sequences and estimating their two-dimensional velocity fields. To solve the problem of lack of sub-grid small-scale structure information in variati...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/1/437 |
| _version_ | 1797431142486376448 |
|---|---|
| author | Haoxuan Xu Jianping Wang Ya Zhang Guo Zhang Zhaolong Xiong |
| author_facet | Haoxuan Xu Jianping Wang Ya Zhang Guo Zhang Zhaolong Xiong |
| author_sort | Haoxuan Xu |
| collection | DOAJ |
| description | The variational optical flow model is used in this work to investigate a subgrid-scale optimization approach for modeling complex fluid flows in image sequences and estimating their two-dimensional velocity fields. To solve the problem of lack of sub-grid small-scale structure information in variational optical flow estimation, we combine the motion laws of incompressible fluids. Introducing the idea of large eddy simulation, the instantaneous motion can be decomposed into large-scale motion and a small-scale turbulence in the data term. The Smagorinsky model is used to model and solve the small-scale turbulence. The improved subgrid scale Horn–Schunck (SGS-HS) optical flow algorithm provides better results in velocity field estimation of turbulent image sequences than the traditional Farneback dense optical flow algorithm. To make the SGS-HS algorithm equally competent for the open channel flow measurement task, a velocity gradient constraint is chosen for the canonical term of the model, which is used to improve the accuracy of the SGS-HS algorithm in velocimetric experiments in the case of the relatively uniform flow direction of the open channel flow field. The experimental results show that our algorithm has better performance in open channel velocimetry compared with the conventional algorithm. |
| first_indexed | 2024-03-09T09:40:40Z |
| format | Article |
| id | doaj.art-537b56abcce94eb4b99a61d14b19ec6a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:40:40Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-537b56abcce94eb4b99a61d14b19ec6a2023-12-02T00:56:54ZengMDPI AGSensors1424-82202022-12-0123143710.3390/s23010437Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image VelocimetryHaoxuan Xu0Jianping Wang1Ya Zhang2Guo Zhang3Zhaolong Xiong4Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, ChinaNanjing Institute of Water Resources and Hydrology Automation, Ministry of Water Resources, Nanjing 210000, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, ChinaThe variational optical flow model is used in this work to investigate a subgrid-scale optimization approach for modeling complex fluid flows in image sequences and estimating their two-dimensional velocity fields. To solve the problem of lack of sub-grid small-scale structure information in variational optical flow estimation, we combine the motion laws of incompressible fluids. Introducing the idea of large eddy simulation, the instantaneous motion can be decomposed into large-scale motion and a small-scale turbulence in the data term. The Smagorinsky model is used to model and solve the small-scale turbulence. The improved subgrid scale Horn–Schunck (SGS-HS) optical flow algorithm provides better results in velocity field estimation of turbulent image sequences than the traditional Farneback dense optical flow algorithm. To make the SGS-HS algorithm equally competent for the open channel flow measurement task, a velocity gradient constraint is chosen for the canonical term of the model, which is used to improve the accuracy of the SGS-HS algorithm in velocimetric experiments in the case of the relatively uniform flow direction of the open channel flow field. The experimental results show that our algorithm has better performance in open channel velocimetry compared with the conventional algorithm.https://www.mdpi.com/1424-8220/23/1/437optical flowsub-grid scalelarge eddy simulationturbulenceopen channel |
| spellingShingle | Haoxuan Xu Jianping Wang Ya Zhang Guo Zhang Zhaolong Xiong Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry Sensors optical flow sub-grid scale large eddy simulation turbulence open channel |
| title | Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry |
| title_full | Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry |
| title_fullStr | Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry |
| title_full_unstemmed | Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry |
| title_short | Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry |
| title_sort | subgrid variational optimized optical flow estimation algorithm for image velocimetry |
| topic | optical flow sub-grid scale large eddy simulation turbulence open channel |
| url | https://www.mdpi.com/1424-8220/23/1/437 |
| work_keys_str_mv | AT haoxuanxu subgridvariationaloptimizedopticalflowestimationalgorithmforimagevelocimetry AT jianpingwang subgridvariationaloptimizedopticalflowestimationalgorithmforimagevelocimetry AT yazhang subgridvariationaloptimizedopticalflowestimationalgorithmforimagevelocimetry AT guozhang subgridvariationaloptimizedopticalflowestimationalgorithmforimagevelocimetry AT zhaolongxiong subgridvariationaloptimizedopticalflowestimationalgorithmforimagevelocimetry |