A Robust Fundamental Matrix Estimation Method Based on Epipolar Geometric Error Criterion

Yan, Kun; Zhao, Rujin; Liu, Enhai; Ma, Yuebo

doi:10.1109/access.2019.2946387

Cited by 8 publications

(5 citation statements)

References 34 publications

(29 reference statements)

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“…Two datasets were used in the experiment: one is the real dataset, and the other is a simulated dataset containing Gaussian noise and outliers. The proposed method is compared with ISSO [30], EGEC [31] and RANSAC [26] methods. In the subsequent experiments, the mean epipolar geometry distance and the mean distance between feature points and the intersection points of epipolar lines are used as the evaluation criteria to evaluate the accuracy of various methods.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The above methods make up for the deficiency of the traditional robust method to some extent, but they also have a fatal problem, that is, the accuracy of these methods deteriorates sharply with the increase in outlier ratio. Later, Yan et al [31] proposed a robust fundamental matrix estimation method based on epipolar geometric error criterion, which eliminated the outliers in the calculation process of the fundamental matrix and improved the calculation efficiency; however, the calculation accuracy of such methods needs to be further improved.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

Liang

Huo

2022

Sensors

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A fundamental matrix estimation based on matching points is a critical problem in epipolar geometry. In this paper, a global fundamental matrix estimation method based on inlier updating is proposed. Firstly, the coplanar constraint was incorporated into the solution of the fundamental matrix to reduce the number of parameters to be solved. Subsequently, an inlier updating matrix was introduced according to the threshold of the epipolar geometry distance to eliminate the potential outliers and obtain a reliable initial value of the fundamental matrix. On this basis, we employed a four-point iterative method to estimate the fundamental matrix and make it satisfy the rank constraint at the same time. Finally, the epipolar geometry in binocular vision was extended to triple-view, and the fundamental matrix obtained in the previous step was globally optimized by minimizing the coordinate deviation between the intersection point and feature point in each group of images. The experiments show that the proposed fundamental matrix estimation method is robust to noise and outliers. In the attitude measurement, the maximum static error was 0.104° and dynamic measurement error was superior to 0.273°, which improved the reconstruction accuracy of feature points. Indoor images were further used to test the method, and the mean rotation angle error was 0.362°. The results demonstrate that the estimation method proposed in this paper has a good practical application prospect in multi-view 3D reconstruction and visual localization.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

Liang

Huo

2022

Sensors

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“…Literature [13] proposed a detection method based on geometric constraints for the extraction of action targets.…”

Section: Related Knowledgementioning

confidence: 99%

Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Yang

2021

Complexity

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Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection. Firstly, the optical flow picture and Red, Green, Blue (RGB) picture obtained by the Lucas-Kanade algorithm are used. Secondly, the data of optical flow pictures and RGB pictures are compressed based on a random projection matrix of compressed sensing, which effectively reduces power consumption. At the same time, based on random projection compression data, it can effectively find the optimal linear representation to reconstruct training samples and test samples. Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer. Experimental results show that the algorithm in this paper significantly improves the recognition rate of multicategory actions and effectively reduces the computational complexity and running time of the recognition algorithm.

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“…The fundamental matrix can be simply computed if only more than seven corresponding points are found in both two views [41]. In order to construct accurate geometry relationship between two views, matching points between them are manually labelled as many as possible, taking the sidelines of the court as references.…”

Section: Multi‐camera 3d Ball Tracking Frameworkmentioning

confidence: 99%

Multi‐camera 3D ball tracking framework for sports video

Wu¹,

Liang³

et al. 2020

IET Image Processing

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Accurate ball tracking in sports is vital for automatic sports analysis yet it is challenging mainly due to the small size and occlusions. This study proposes a novel multi-camera 3D ball tracking (MBT) framework for sports video. The proposed framework consists of four parts: 2D ball detection, 2D ball tracking, 3D position fusion, and 3D ball tracking. In 2D aspect, the multi-scale features are introduced to enhance the 2D ball detection, and the 2D ball tracking is also improved by exploring cross-view information to handle the occlusion and timely updating tracking model with detection results to alleviate the problem of tracking drift. For 3D ball, a novel 3D position fusion method is proposed to optimise the ball position and the 3D ball tracking approach with improved Kalman filter is finally applied to ensure a smooth 3D ball trajectory. Moreover, compared to the existing products in commercial, the proposed framework does not require any special equipment and is thus low cost. Extensive experiments for 2D and 3D ball on a public dataset demonstrate that the proposed framework is robust to ball tracking in sports video, even in the presence of environmental interferences, substantial occlusions, and even calibration errors.

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A Robust Fundamental Matrix Estimation Method Based on Epipolar Geometric Error Criterion

Cited by 8 publications

References 34 publications

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Multi‐camera 3D ball tracking framework for sports video

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