Monocular Epipolar Constraint for Optical Flow Estimation

Mohamed, Mostafa; Mirabdollah, M. Hossein; Mertsching, Bärbel

doi:10.1007/978-3-319-68345-4_6

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The KITTI 2015 benchmark [13] [23] 3.38 % 10.06 % 0.9 px 2.9 px 100.00 % 11 s 1 core @ 3.5 Ghz SDF [34] 3.80 % 7.69 % 1.0 px 2.3 px 100.00 % TBA 1 core @ 2.5 Ghz MotionSLIC [35] 3.91 % 10.56 % 0.9 px 2.7 px 100.00 % 11 s 1 core @ 3.0 Ghz RicFlow [36] 4.96 % 13.04 % 1.3 px 3.2 px 100.00 % 5 s 1 core @ 3.5 Ghz CPM2 [37] 5.60 % 13.52 % 1.3 px 3.3 px 100.00 % 4 s 1 core @ 2.5 Ghz CPM-Flow [37] 5.79 % 13.70 % 1.3 px 3.2 px 100.00 % 4.2s 1 core @ 3.5 Ghz MEC-Flow [38] 6.95 % 17.91 % 1.8 px 6.0 px 100.00 % 3 s 1 core @ 2.5 Ghz DeepFlow [18] 7 challenges of dynamic scene objects (vehicles). These exhibit far larger pixel displacements in some areas resulting in lower algorithm performance on KITTI 2015 (Table 3) than on KITTI 2012 (Table 2).…”

Section: Benchmark Comparison -Kitti 2015mentioning

confidence: 99%

Degraf-Flow: Extending Degraf Features for Accurate and Efficient Sparse-To-Dense Optical Flow Estimation

Stephenson

Breckon

Katramados

2019

2019 IEEE International Conference on Image Processing (ICIP)

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Modern optical flow methods make use of salient scene feature points detected and matched within the scene as a basis for sparse-to-dense optical flow estimation. Current feature detectors however either give sparse, non uniform point clouds (resulting in flow inaccuracies) or lack the efficiency for frame-rate real-time applications. In this work we use the novel Dense Gradient Based Features (DeGraF) as the input to a sparse-to-dense optical flow scheme. This consists of three stages: 1) efficient detection of uniformly distributed Dense Gradient Based Features (DeGraF) [1]; 2) feature tracking via robust local optical flow [2]; and 3) edge preserving flow interpolation [3] to recover overall dense optical flow. The tunable density and uniformity of DeGraF features yield superior dense optical flow estimation compared to other popular feature detectors within this three stage pipeline. Furthermore, the comparable speed of feature detection also lends itself well to the aim of real-time optical flow recovery. Evaluation on established real-world benchmark datasets show test performance in an autonomous vehicle setting where DeGraF-Flow shows promising results in terms of accuracy with competitive computational efficiency among non-GPU based methods, including a marked increase in speed over the conceptually similar EpicFlow approach [3].

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Section: Benchmark Comparison -Kitti 2015mentioning

confidence: 99%

Degraf-Flow: Extending Degraf Features for Accurate and Efficient Sparse-To-Dense Optical Flow Estimation

Stephenson

Breckon

Katramados

2019

2019 IEEE International Conference on Image Processing (ICIP)

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“…Using the size of the target as the top-level feature baseline, the baseline information is transmitted to the features of each level, and then the real proportion of the displacement T in the motion of the monocular camera is solved, so as to solve the scale uncertainty and reduce the drift. We note that the feature matching in traditional visual odometry often directly uses the extraction of feature points or pixel gradient information such as oriented fast and rotated brief (ORB) [16], and then uses the epipolar constraint [17] and random consistency algorithm [18] to obtain reliable motion matching. At the same time, due to the scale equivalence of the essential matrix E in the epipolar constraint, the resulting displacement T also faces the scale uncertainty problem.…”

Section: Introductionmentioning

confidence: 99%

MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

et al. 2022

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In this study, a multi-level scale stabilizer intended for visual odometry (MLSS-VO) combined with a self-supervised feature matching method is proposed to address the scale uncertainty and scale drift encountered in the field of monocular visual odometry. Firstly, the architecture of an instance-level recognition model is adopted to propose a feature matching model based on a Siamese neural network. Combined with the traditional approach to feature point extraction, the feature baselines on different levels are extracted, and then treated as a reference for estimating the motion scale of the camera. On this basis, the size of the target in the tracking task is taken as the top-level feature baseline, while the motion matrix parameters as obtained by the original visual odometry of the feature point method are used to solve the real motion scale of the current frame. The multi-level feature baselines are solved to update the motion scale while reducing the scale drift. Finally, the spatial target localization algorithm and the MLSS-VO are applied to propose a framework intended for the tracking of target on the mobile platform. According to the experimental results, the root mean square error (RMSE) of localization is less than 3.87 cm, and the RMSE of target tracking is less than 4.97 cm, which demonstrates that the MLSS-VO method based on the target tracking scene is effective in resolving scale uncertainty and restricting scale drift, so as to ensure the spatial positioning and tracking of the target.

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Robust Optical Flow Estimation Using the Monocular Epipolar Geometry

Mohamed

Mertsching

2019

Lecture Notes in Computer Science

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Monocular Epipolar Constraint for Optical Flow Estimation

Cited by 3 publications

References 15 publications

Degraf-Flow: Extending Degraf Features for Accurate and Efficient Sparse-To-Dense Optical Flow Estimation

Degraf-Flow: Extending Degraf Features for Accurate and Efficient Sparse-To-Dense Optical Flow Estimation

MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

Robust Optical Flow Estimation Using the Monocular Epipolar Geometry

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