Efficient motion vector outlier removal for global motion estimation

Dinh, Toan Nguyen; Lee, Guee-Sang

doi:10.1109/icme.2011.6011881

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The kernel bandwidth parameters used were (ℎ , ℎ ) = (8,4). Comparative studies is carried out with the following methods to validate our proposed outlier removal method using mean shift clustering: i) Gradient descent (Plain GD) [5], ii) Filter method (FLT GD) [8], iii) Cascade-of-rejectors (CAS GD) [6], iv) Tensor Voting method (TV GD) [10].…”

Section: Resultsmentioning

confidence: 99%

Mean shift clustering based outlier removal for global motion estimation

Okade

Biswas

2013

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

View full text Add to dashboard Cite

This paper investigates a novel motion vector outlier rejection method based on using mean shift clustering on block motion vectors. The accuracy of compressed domain global motion estimation techniques is largely influenced by its ability to counter the outlier motion vectors. These outliers occur in the block motion vector field due to moving objects, noise or due to large matching errors as a result of the encoders priority on rate distortion optimization. In the present work it is shown that by using mean shift clustering on block motion vectors, those clusters which correspond to outlier motion vectors can be identified. Once detected these clusters are kept out of the global motion estimation process thereby increasing the robustness of estimated camera parameters. The proposed method is compared with existing state-of-the-art outlier removal methods using synthetic and real video sequences to establish and validate its superiority.

show abstract

Section: Resultsmentioning

confidence: 99%

Mean shift clustering based outlier removal for global motion estimation

Okade

Biswas

2013

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

View full text Add to dashboard Cite

show abstract

“…In [64], a motion vector field was encoded as 2D tensors and tensor voting was used to remove outliers.…”

Section: Motion Analysismentioning

confidence: 99%

Perceptual grouping by tensor voting: a comparative survey of recent approaches

Maggiori¹,

Manterola

Fresno³

2015

IET Computer Vision

View full text Add to dashboard Cite

Tensor voting is a computational framework that addresses the problem of perceptual organisation. It was designed to convey human perception principles into a unified framework that can be adapted to extract visually salient elements from possibly noisy or corrupted images. The original formulation featured some concerns that made it difficult or impractical to be applied directly. Therefore, several partial or total theoretical reformulations or augmentations have been proposed. These almost parallel publication were presented in different directions, with different priorities and even in a different notation. Thus, the authors observed the need for a coherent description and comparison of the different proposals. This work, after describing the original approach of tensor voting, reviews and explains a number of high impact theoretical modifications in a self‐contained manner and including possible future directions of work. The authors have selected and organised a number of formulations and unified the way the problem is addressed across the different proposals. The aim of this study is to contribute with a modern comprehensive source of information on the theoretical aspects of tensor voting.

show abstract

“…On similar lines, camera motion parameters were estimated directly from the MPEG domain using least squares solution and its application to annotation of basketball videos was investigated in [8]. Recently, a motion vector outlier removal method for GME was based on 2-D tensor voting process among motion vectors [9]. GME in compressed domain following the generic helmholtz principle to counter the outliers was proposed in [10].…”

Section: Introductionmentioning

confidence: 99%

Fast camera motion estimation using discrete wavelet transform on block motion vectors

Okade

Biswas

2012

2012 Picture Coding Symposium

View full text Add to dashboard Cite

In this paper we propose to use the discrete wavelet transform on the block motion vector field for estimating the camera (global) motion parameters in the compressed domain. By taking the wavelet transform of the block motion vector field we get the decomposition of the motion vectors into wavelet sub-bands. The LL sub-band gives us the average motion which we assume is mainly due to the background (camera) motion. By using gradient descent regression on LL sub-band wavelet coefficients we extract the camera motion parameters. Our results show that LL sub-band is enough to estimate the camera motion parameters unlike previous methods which utilize the entire block motion vector field to carry out the estimation process. Our experimental results demonstrate significant gain in processing time at the cost of marginal drop of estimation accuracy for the camera motion parameters. Our proposed technique finds its application in video indexing and video shot segmentation where fast camera motion estimation is warranted.

show abstract

Efficient motion vector outlier removal for global motion estimation

Cited by 6 publications

References 14 publications

Mean shift clustering based outlier removal for global motion estimation

Mean shift clustering based outlier removal for global motion estimation

Perceptual grouping by tensor voting: a comparative survey of recent approaches

Fast camera motion estimation using discrete wavelet transform on block motion vectors

Contact Info

Product

Resources

About