Robust optical flow estimation based on wavelet

Zheng, Jia; Wang, Hongyan; Pei, Bingnan

doi:10.1007/s11760-019-01476-7

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…Table 3 demonstrates that in the outlier elimination algorithm comparison experiments, the dataset Odataset includes three motion pattern datasets (Normal amplitude motion scene dataset n_datas, small amplitude motion scene dataset s_datas, and large amplitude motion scene dataset l_datas), each of which consists of the inlier optical flow values (u gt , v gt ) and the outlier optical flow values (u ft , v ft ). [34] bamboo_2(frame_0001 ∼ 0010) [34] temple_2.1(frame_0001 ∼ 0010) [34] temple_2.2(frame_0011 ∼ 0021) [34] 15-50 pixels s_datas 30 group 100 alley_1(frame_0001 ∼ 0016) [34] FlyingChairs (6,7,11,12,16,33,40,78,82 104) [35] Middlebury(grov(2 ∼ 3)、urban(2 ∼ 3)、venus) [36] >15 pixels l_datas 40 group 100 ambush_2.1(frame_0001 ∼ 0010) [34] ambush_2.2(frame_0010 ∼ 0021) [34] market_5.1(frame_0001 ∼ 0010) [34] market_5.2(frame_0011 ∼ 0021) [34] <50 pixels…”

Section: Datasetsmentioning

confidence: 99%

“…These light displacement group values seriously affect the analysis results and need to be eliminated. Currently, researchers have widely used methods based on threshold [7][8][9], local area consistency [10,11], and statistics [12] to eliminate outliers in the optical flow values.…”

Section: Introductionmentioning

confidence: 99%

“…A threshold is set, and the pixel points with an optical flow value exceeding the threshold is defined as outliers. Zheng et al [7] combined median filtering and total variational regularization techniques to remove outliers and noise from optical flow values, improving the robustness and precision of optical flow estimation. Silva et al [8] utilized a wavelet transform to decompose the signal into different frequency components and performed threshold processing based on the statistical characteristics of each component to eliminate optical flow errors caused by noise.…”

Section: Introductionmentioning

confidence: 99%

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Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

Wang,

Li,

Wang

et al. 2024

Mach. Learn.: Sci. Technol.

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During the tracking of moving targets in dynamic scenes, efficiently handling outliers in the optical flow and maintaining robustness across various motion amplitudes represents a critical challenge. So far, studies have used thresholding and local consistency based approaches to deal with optical outliers. However, there is subjectivity through expert-defined thresholds or delineated regions, and therefore these methods do not perform consistently enough under different target motion amplitudes. Other studies have focused on complex statistical-mathematical modelling which, although theoretically valid, requires significant computational resources. Aiming at the above problems this paper proposes a new method to calculate the optical outliers by using stochastic neighborhood graph combined with the Borda counting method, which reduces the computation amount on the basis of objectively eliminating the outliers. Sparse optical flow values are used as the overall population and the outlier and inlier sparse optical flow values are used as samples. Analyze the dissimilarity between sparse optical flow data points, obtaining the dissimilarity matrix, introducing the Gaussian function to smooth and reduce the dimensionality of the dissimilarity matrix, and then normalizing the smoothing matrix to generate the binding matrix, where the probability sum of each node to other nodes in the matrix is equal to 1. Stochastic neighborhood graphs are then generated based on a binding matrix to obtain the outlier probabilities of data points in different neighborhood graphs, and outlier samples are obtained based on the probability. To avoid the subjectivity of the expert thresholds, the outlier probabilities are weighted and ranked to calculate the data point Borda scores to obtain accurate optical outliers. The experimental results show that the method in this paper is robust to different amplitude motions and real scenarios, and the accuracy, precision and recall of outliers elimination are better than the current mainstream algorithms.

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

Wang,

Li,

Wang

et al. 2024

Mach. Learn.: Sci. Technol.

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“…Indeed, the shape-driven optical flow channel is designed to capture building morphology and motion details, generating essential pseudo-labels for semi-supervised studies. However, it has limitations in understanding texture intricacies, fine details, and contextual cues, and is sensitive to illumination fluctuations and occlusion [36][37][38].…”

Section: Improved Deeplabv3+ Module In Dual-channel Generatormentioning

confidence: 99%

Dual-Channel Semi-Supervised Adversarial Network for Building Segmentation from UAV-Captured Images

Zhang,

Wu,

Man

et al. 2023

Remote Sensing

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Accurate building extraction holds paramount importance in various applications such as urbanization rate calculations, urban planning, and resource allocation. In response to the escalating demand for precise low-altitude unmanned aerial vehicle (UAV) building segmentation in intricate scenarios, this study introduces a semi-supervised methodology to alleviate the labor-intensive process of procuring pixel-level annotations. Within the framework of adversarial networks, we employ a dual-channel parallel generator strategy that amalgamates the morphology-driven optical flow estimation channel with an enhanced multilayer sensing Deeplabv3+ module. This approach aims to comprehensively capture both the morphological attributes and textural intricacies of buildings while mitigating the dependency on annotated data. To further enhance the network’s capability to discern building features, we introduce an adaptive attention mechanism via a feature fusion module. Additionally, we implement a composite loss function to augment the model’s sensitivity to building structures. Across two distinct low-altitude UAV datasets within the domain of UAV-based building segmentation, our proposed method achieves average mean pixel intersection-over-union (mIoU) ratios of 82.69% and 79.37%, respectively, with unlabeled data constituting 70% of the overall dataset. These outcomes signify noteworthy advancements compared with contemporaneous networks, underscoring the robustness of our approach in tackling intricate building segmentation challenges in the domain of UAV-based architectural analysis.

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“…14 Since then, many optical flow methods have been proposed. 15,16 However, the environment of low and non-uniform illumination in fully mechanized coal mining face has seriously restricted the optical-flow application based on visible light imaging, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Online shearer-onboard personnel detection method for the intelligent fully mechanized mining face

Wei

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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As unmanned coal mining technology gradually replaces the traditional mechanized coal mining technology, shearer operation mode is changed from local control to remote control in intelligent fully mechanized mining face. In remote control mode, it is difficult to protect the personnel who straying into the shearer operator space without observation and reminder from the shearer operator. Hence, it is necessary to establish an intelligent personnel detection method to protect the safety of coal miners in intelligent unmanned mining face. The environment of low and non-uniform illumination in fully mechanized coal mining face has seriously restricted the application of personnel detection technology based on visible light imaging. Therefore, a personnel detection method based on infrared thermal imaging is proposed in this paper to solve the disadvantages of using visible light imaging in downhole applications. On this basis, a spatiotemporal guided filter is proposed to harmonize the relationship between edge-preserving and noise-removing. Then, an improved Lucas-Kanade method based on the adaptive-size window is utilized to achieve a more robust personnel detection. Moreover, the personnel detection in the shearer operating space is realized based on epipolar geometry and morphology processing. Finally, the laboratory experiment and industrial test are carried out to evaluate the proposed method, and the results indicate the feasibility and superiority of the proposed methods which show considerable application prospects.

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Robust optical flow estimation based on wavelet

Cited by 6 publications

References 15 publications

Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

Dual-Channel Semi-Supervised Adversarial Network for Building Segmentation from UAV-Captured Images

Online shearer-onboard personnel detection method for the intelligent fully mechanized mining face

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