Robust selection of parametric motion models in image sequences

Parametric motion models are commonly used in image sequence analysis for different tasks. A robust estimation framework is usually required to reliably compute the motion model. The choice of the right model is also important. However, dealing simultaneously with both issues remains an open question. We propose a robust motion model selection method with two variants, which relies on the Fisher test. We also derive an interpretation of it as a robust Mallows' CP criterion. The resulting criterion is straightforward to compute. We have conducted a comparative experimental evaluation on different image sequences demonstrating the interest and the efficiency of the proposed method.

[1]  H. Künsch,et al.  On model selection via stochastic complexity in robust linear regression , 1998 .

[2]  B. Ripley,et al.  Robust Statistics , 2018, Wiley Series in Probability and Statistics.

[3]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

[4]  Patrick Bouthemy,et al.  Optical flow modeling and computation: A survey , 2015, Comput. Vis. Image Underst..

[5]  Alireza Bab-Hadiashar,et al.  Motion analysis: model selection and motion segmentation , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[6]  Philip H. S. Torr.  An assessment of information criteria for motion model selection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Alireza Bab-Hadiashar,et al.  A comparative study of model selection criteria for computer vision applications , 2008, Image Vis. Comput..

[8]  Elvezio Ronchetti,et al.  A Robust Version of Mallows's C P , 1994 .

[9]  Patrick Bouthemy,et al.  Derivation of qualitative information in motion analysis , 1990, Image Vis. Comput..

[10]  E. Ronchetti.  Robust model selection in regression , 1985 .

[11]  Samuel Müller,et al.  Outlier Robust Model Selection in Linear Regression , 2005 .

[12]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[13]  José A.F. Machado,et al.  Robust Model Selection and M-Estimation , 1993, Econometric Theory.

[14]  Jiaolong Yang,et al.  Dense, accurate optical flow estimation with piecewise parametric model , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  C. Mallows.  Some Comments on Cp , 2000, Technometrics.

[16]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[17]  H. Akaike.  A new look at the statistical model identification , 1974 .

[18]  Jean-Marc Odobez,et al.  Robust Multiresolution Estimation of Parametric Motion Models , 1995, J. Vis. Commun. Image Represent..

[19]  C. Agostinelli.  Robust model selection in regression via weighted likelihood methodology , 2002 .

[20]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[21]  Tobias Senst,et al.  Robust Local Optical Flow for Feature Tracking , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Daniel Cremers,et al.  Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation , 2005, International Journal of Computer Vision.

[23]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[24]  Kenichi Kanatani,et al.  Geometric Information Criterion for Model Selection , 1998, International Journal of Computer Vision.

[25]  Harpreet S. Sawhney,et al.  Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding , 1995, Proceedings of IEEE International Conference on Computer Vision.

[26]  G. Roussas.  A course in mathematical statistics , 1997 .

[27]  Patrick Bouthemy,et al.  Improved Motion Description for Action Classification , 2016, Front. ICT.

[28]  David R. Anderson,et al.  Model Selection and Multimodel Inference , 2003 .

[29]  Peter Meer,et al.  ROBUST TECHNIQUES FOR COMPUTER VISION , 2004 .

[30]  Harry Shum,et al.  Full-frame video stabilization with motion inpainting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.