An Improved Fractional-Order Optical Flow Model for Motion Estimation

Zhu, Bin; Tian, Lianfang; Du, Qian; Wu, Qiuxia; Shi, Lixin

doi:10.1155/2018/6278719

Cited by 3 publications

(7 citation statements)

References 22 publications

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“…To testify the superiority and practicability of our optical flow model, Middlebury dataset [34], MPI_Sintel dataset [35], KITTI dataset [36], and an outdoor real scene video are used in experiments. Vertical comparisons are employed to explain the model evolution process; these models include HS model [1], LSCOFM [15], FOVOFM [16], and DFOVOFM [17]. ree widely used high performance models are employed for horizontal comparison: HAST [37], MDP_Flow [38], and PH_Flow [39].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Middlebury dataset, MPI_Sintel dataset, and KITTI dataset can be found in references [17][18][19]. Our own outdoor video can be easily obtained by a normal RGB camera.…”

Section: Data Availabilitymentioning

confidence: 99%

“…e fractional-order variational optical flow model (FOVOFM) is proposed by Chen et al [16], which uses the fractional-order smoothness constraint in the smoothness term of the HS model. e dual fractional order variational optical flow model (DFOVOFM) is proposed by Zhu et al [17], which the fractional-order derivative is applied both in data term and smoothness term of the HS model. e fractional-order TV-L1 optical flow model was proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Fractional-Order Variational Optical Flow Model Combining Structure Tensor

Zhu

Wang

Tian

et al. 2021

Mathematical Problems in Engineering

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Dealing with problems of illumination changes in optical flow estimation, an improved variational optical flow model is proposed in this paper. The local structure constancy constraint (LSCC) is applied in the data term of the traditional HS (Horn & Schunck) optical flow model to substitute the brightness constancy constraint. The fractional-order smoothness constraint (FSC) is applied in the smoothness term of the HS model. Then, the detailed calculation processes from the optical flow model to the optical flow value are explained. The structure tensor in LSCC is an image feature that is constant in the illumination changes scene. The fractional differential coefficient in FSC can fuse the local neighborhood optical flow vector into the optical flow vector of the target pixel, which can improve the integrity of the motion region with the same motion speed. Combining LSCC with FSC, our improved optical flow model can obtain an accurate optical flow field with clear outline in the illumination abnormity scene. The experimental results show that, compared with other optical flow models, our model is more suitable for the illumination changes scene and can be employed in outdoor motion detection projects.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Middlebury dataset, MPI_Sintel dataset, and KITTI dataset can be found in references [17][18][19]. Our own outdoor video can be easily obtained by a normal RGB camera.…”

Section: Data Availabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Fractional-Order Variational Optical Flow Model Combining Structure Tensor

Zhu

Wang

Tian

et al. 2021

Mathematical Problems in Engineering

Self Cite

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show abstract

“…In our algorithm, we choose

λ \in false[50, 100 false]

in relatively clear images, and

λ \in false[100, 150 false]

in relatively blurry images. Generally, the value of

β = 1.3

or 1.4 [8]. The accuracy when

β = 1.3

is relatively higher than when

β = 1.4

, while the latter case needs fewer iteration times.…”

Section: Experimental Results and Performance Evaluationmentioning

confidence: 99%

“…The largest size of the mask should be <

11 \times 11

, i.e. L = 5; when SNR becomes small, the mask size L should be decreased, and the smallest L is 2 [8].…”

Section: Adaptive Fractional‐order Differential Maskmentioning

confidence: 99%

Adaptive dual fractional‐order variational optical flow model for motion estimation

Zhu

et al. 2019

IET Computer Vision

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Insufficient illumination and illumination variation in image sequences make it challenging for algorithms to obtain clear outlines for objects in motion. This study proposes a high-performance adaptive dual fractional-order variational optical flow model which could be used to resolve these issues. The proposed method revitalises the original dual fractional-order optical flow model and adopts a fractional differential mask in both the data and smoothness terms of the traditional Horn-Schunck model. The main innovation of this work is to fit a flow field regional to a variety of fractional-order differential masks. The domain of each region is determined adaptively. The order and size of the fractional-order differential masks for each region are adjusted by image signal to noise ratio while the shape of the fractional-order differential mask is regulated to prevent interference from surrounding regions. Adjusting the fractional-order differential mask adaptively enables the proposed method to accurately segment motion objects in poor and variable illumination regions as well. The experimental results show that our algorithm outperforms the current state-of-the-art algorithms on low-light real scene videos and also achieves competitive results on the Middlebury, KITTI and MPI Sintel public benchmarks.

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Research on HS Optical Flow Algorithm Based on Motion Estimation Optimization

Wei¹,

Zou²,

Bo³

et al. 2018

JCC

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The amount of computation for detecting moving objects by the optical flow algorithm is large. The optical flow information in the smooth region cannot be detected by the optical flow algorithm, and it is susceptible to noise in a complicated environment. In this study, an optimized Horn-Schunck (HS) optical flow algorithm based on motion estimation is proposed. To detect Harris corner in the image, the proposed algorithm is used in combination with the motion estimation algorithm based on macroblock to determine the region of interest (ROI) [1]. The ROI is then used as the initial motion vector for HS calculation to obtain the optical flow information. Filtering is conducted to eliminate the background noise. Experimental result shows that the application of the proposed algorithm improves the computational speed, avoids the interference of background noise, and enhances the robustness of HS. Moreover, the algorithm solves the problem rooted in the inability of the HS algorithm to detect the smooth part of optical flow information [2].

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An Improved Fractional-Order Optical Flow Model for Motion Estimation

Cited by 3 publications

References 22 publications

An Improved Fractional-Order Variational Optical Flow Model Combining Structure Tensor

An Improved Fractional-Order Variational Optical Flow Model Combining Structure Tensor

Adaptive dual fractional‐order variational optical flow model for motion estimation

Research on HS Optical Flow Algorithm Based on Motion Estimation Optimization

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