A Joint Approach to Global Motion Estimation and Motion Segmentation From a Coarsely Sampled Motion Vector Field

Chen, Yue-Meng; Bajić, Ivan V.

doi:10.1109/tcsvt.2011.2148490

Cited by 40 publications

(19 citation statements)

References 27 publications

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“…The GDV for a slice is computed at the encoder side that is then transferred to the decoder side. In this paper, GDVs are calculated based on the perspective motion model [24][25]. Assume that each adjacent view has one GDV per frame.…”

Section: The Proposed Motion Vector and Disparity Prediction For Multmentioning

confidence: 99%

Novel Motion and Disparity Prediction for Multi-view Video Coding

Lim

Nam²,

Sim³

2014

IEIE Transactions on Smart Processing and Computing

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This paper presents an efficient motion and disparity prediction method for multi-view video coding based on the high efficient video coding (HEVC) standard. The proposed method exploits inter-view candidates for effective prediction of the motion or disparity vector to be coded. The inter-view candidates include not only the motion vectors of adjacent views, but also global disparities across views. The motion vectors coded earlier in an adjacent view were found to be helpful in predicting the current motion vector to reduce the number of bits used in the motion vector information. In addition, the proposed disparity prediction using the global disparity method was found to be effective for interview predictions. A multi-view version based on HEVC was used to evaluate the proposed algorithm, and the proposed correspondence prediction method was implemented on a multi-view platform based on HEVC. The proposed algorithm yielded a coding gain of approximately 2.9% in a high efficiency configuration random access mode.

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Section: The Proposed Motion Vector and Disparity Prediction For Multmentioning

confidence: 99%

Novel Motion and Disparity Prediction for Multi-view Video Coding

Lim

Nam²,

Sim³

2014

IEIE Transactions on Smart Processing and Computing

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“…In [17] motion vectors which are found in regions with little or no texture or a moving object boundary and regions with repetitive texture patterns are deemed as "noisy". A filter is used to examine the magnitude and phase difference between a motion vector and its 8-adjacency neighbors and then a fraction of the available motion vectors was removed based on spatial dissimilarity.…”

Section: Foreground-background Shape Initializationmentioning

confidence: 99%

Motion segmentation initialization strategies for bi-directional inter-frame prediction

Ahmmed

Naman

et al. 2013

2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP)

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Experimental results and the latest standards have proved that segmentation based video coding systems can outperform the traditional block-based video coding systems. However, this approach requires the simultaneous estimation of both the shape and motion of moving objects in a video scene. In most of the cases neither the shape nor the motion are known initially. Another critical aspect of this tightly-coupled relationship is that inaccurate motion estimation may cause poor segmentation and erroneous segmentation may negatively impact motion estimation. While some of the existing approaches require user intervention and some use clues such as depth, colour or occlusion to separate the foreground from the background, we propose to use motion reliability information for this purpose. This is because the ingredients necessary for the calculation of motion reliability are the by-product of block-based motion estimation and compensation between the reference frames. Therefore, they require very little or no increase in the computational overhead. In this paper, we explore several motion segmentation initialization strategies based on motion reliability. The performances of these initialization approaches are investigated, in terms of the PSNR, for the predicted inter-frames.

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“…A common scheme in 3D motion segmentation is to use optical flow or trajectories as a cue. As optical flow can be directly used for clustering or to compensate for the camera motion, the pixelwise model are often used for segmentation [1] [2] [3] [4].…”

Section: Introductionmentioning

confidence: 99%

“…Chen and Bajic [1] proposed an outlier rejection filter that explicitly filters motion vectors by checking their similarity in a pre-defined window. Chen and Bajic [2], and Qian and Bajic [3] proposed a joint global motion estimation, which iteratively update the inlier model by segmenting outliers out. Although these methods have achieved great progress in dealing with independent motions, they are very likely to over-segment ob- jects due to the motion bias introduced by camera motion [2].…”

Section: Introductionmentioning

confidence: 99%

“…Chen and Bajic [2], and Qian and Bajic [3] proposed a joint global motion estimation, which iteratively update the inlier model by segmenting outliers out. Although these methods have achieved great progress in dealing with independent motions, they are very likely to over-segment ob- jects due to the motion bias introduced by camera motion [2]. Narayana et al [4] proposed a method using the direction of motion flow only.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inlier Estimation for Moving Camera Motion Segmentation

Liang

Zhang

Matsuyama

2015

Computer Vision -- ACCV 2014

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Abstract. In moving camera videos, motion segmentation is often performed on the optical flow. However, there exist two challenges: 1) Camera motions lead to three primary flows in optical flow: translation, rotation, and radial flow. They are not all solved in existing frameworks under Cartesian coordinate system; 2) A moving camera introduces 3D motion, the depth discontinuities cause the motion discontinuities that severely confuse the motion segmentation. Meanwhile, the mixture of the camera motion and moving objects' motions make indistinctness between foreground and background. In this work, our solution is to find a low order polynomial to model the background flow field due to its coherence. To this end, we first amend the Helmholts-Hodge Decomposition by adding coherence constraints, which can handle translation, rotation, and radial flow fields under a unified framework. Secondly, we introduce an Incoherence Map and a progressive Quad-Tree partition to reject moving objects and motion discontinuities. Finally, the low order polynomial is achieved from the rest flow samples on two potentials in HHD. We present results on more than twenty videos from four benchmarks. Extensive experiments demonstrate a better performance in dealing with challenging scenes with complex backgrounds. Our method improves the segmentation accuracy of state-of-the-art by 10% ∼ 30%.

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A Joint Approach to Global Motion Estimation and Motion Segmentation From a Coarsely Sampled Motion Vector Field

Cited by 40 publications

References 27 publications

Novel Motion and Disparity Prediction for Multi-view Video Coding

Novel Motion and Disparity Prediction for Multi-view Video Coding

Motion segmentation initialization strategies for bi-directional inter-frame prediction

Inlier Estimation for Moving Camera Motion Segmentation

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