Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry

Xu, Haoxuan; Wang, Jianping; Zhang, Ya; Guo, Zhang; Xiong, Zhao-Long

doi:10.3390/s23010437

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…OFV is based on the constant brightness assumption between consecutive images and obtains motion vectors by pixel shifting between adjacent images. Based on the variational optical flow model, Xu et al [10] proposed a subgrid-scale variational optical flow algorithm that added large vortex simulation and flow velocity constraints derived from energy conservation conditions in the regularization term. This solved the problem of missing information about the small-scale structure of the subgrid in variational optical flow estimation based on a grid scale.…”

Section: Introductionmentioning

confidence: 99%

A method of applying deep learning based optical flow algorithm to river flow discharge measurement

Wang,

Liu,

Ouyang

et al. 2024

Meas. Sci. Technol.

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River flow discharge monitoring is one of the critical tasks performed at hydrological stations. The Large-scale Particle Image Velocimetry (LSPIV) method widely used in hydrological stations is often limited by a lack of floating objects and has a high computational complexity. The Space-time Image Velocimetry (STIV) method is susceptible to noise interference and requires high stability of the flow over time. This paper proposes a flow measurement method based on the Recurrent All-pairs Field Transforms for Optical Flow (RAFT) algorithm. The 4D correlation volume pyramid in the RAFT network structure can effectively handle changing and complex flow conditions. The Convolutional Block Attention Module (CBAM) is introduced into the optical flow update module after the 4D correlation volume pyramid, enhancing the ability to capture complex flow surface information. Additionally, feature extraction adds Deformable Convolution (DCN) to expand the receptive field of the flow image, which has better adaptability in non-rigid motion. To validate the effectiveness of the new method (RAFT-D-C), this paper conducts comparative experiments with both existing and new methods. The experimental results show that RAFT-D-C has relative errors of 2.13% and 4.41% for the average flow velocity of two rivers and relative errors of 2.19% and 3.05% for the total discharge, respectively. RAFT-D-C provides improved accuracy compared to other methods and requires less computational run time than the frequently used LSPIV method.

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Section: Introductionmentioning

confidence: 99%

A method of applying deep learning based optical flow algorithm to river flow discharge measurement

Wang,

Liu,

Ouyang

et al. 2024

Meas. Sci. Technol.

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Application of a Hybrid Particle Image Velocimetry Method Based on Window Function in the Field of Turbulence

Guo,

Gao,

Xiao

et al. 2024

Lecture Notes in Computer Science

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Non-Intrusive Water Surface Velocity Measurement Based on Deep Learning

An,

Du,

et al. 2024

Water

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Accurate assessment of water surface velocity (WSV) is essential for flood prevention, disaster mitigation, and erosion control within hydrological monitoring. Existing image-based velocimetry techniques largely depend on correlation principles, requiring users to input and adjust parameters to achieve reliable results, which poses challenges for users lacking relevant expertise. This study presents RivVideoFlow, a user-friendly, rapid, and precise method for WSV. RivVideoFlow combines two-dimensional and three-dimensional orthorectification based on Ground Control Points (GCPs) with a deep learning-based multi-frame optical flow estimation algorithm named VideoFlow, which integrates temporal cues. The orthorectification process employs a homography matrix to convert images from various angles into a top-down view, aligning the image coordinates with actual geographical coordinates. VideoFlow achieves superior accuracy and strong dataset generalization compared to two-frame RAFT models due to its more effective capture of flow velocity continuity over time, leading to enhanced stability in velocity measurements. The algorithm has been validated on a flood simulation experimental platform, in outdoor settings, and with synthetic river videos. Results demonstrate that RivVideoFlow can robustly estimate surface velocity under various camera perspectives, enabling continuous real-time dynamic measurement of the entire flow field. Moreover, RivVideoFlow has demonstrated superior performance in low, medium, and high flow velocity scenarios, especially in high-velocity conditions where it achieves high measurement precision. This method provides a more effective solution for hydrological monitoring.

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Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry

Cited by 3 publications

References 28 publications

A method of applying deep learning based optical flow algorithm to river flow discharge measurement

A method of applying deep learning based optical flow algorithm to river flow discharge measurement

Application of a Hybrid Particle Image Velocimetry Method Based on Window Function in the Field of Turbulence

Non-Intrusive Water Surface Velocity Measurement Based on Deep Learning

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