Displacement Following of Hidden Objects in a Video Sequence

In a video sequence, computing the motion of an object requires the continuity of the apparent velocity field. This property does not hold when the object is hidden by an occlusion during its motion. The minimization of an energy functional leads to a simple algorithm which allows the recovery of the most likely trajectory of the occluded object from optical flow data at the border of the occlusion. Optical flow used for developing our method is an improvement on any variational technique of computing it. This improvement is based on a multichannel segmentation.

[1]  Jean-Michel Morel,et al.  Level lines based disocclusion , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[2]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[3]  Jean-Michel Morel,et al.  Variational methods in image segmentation , 1995 .

[4]  Guillermo Sapiro,et al.  Filling-in by joint interpolation of vector fields and gray levels , 2001, IEEE Trans. Image Process..

[5]  Joachim Weickert,et al.  Reliable Estimation of Dense Optical Flow Fields with Large Displacements , 2000, International Journal of Computer Vision.

[6]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[7]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[8]  Isabelle Herlin,et al.  Non Uniform Multiresolution Method for Optical Flow and Phase Portrait Models: Environmental Applications , 2004, International Journal of Computer Vision.

[9]  Olivia Sanchez.  Désoccultation de séquences vidéo : aspects mathématique et numérique , 2002 .

[10]  Julio Esclarín Monreal,et al.  A PDE model for computing the optimal flow , 1999 .

[11]  R. DERICHE,et al.  A Mathematical Study of the Regularized Optical Flow Problem in the Space Bv (() , 1997 .

[12]  R. Deriche,et al.  Les EDP en traitement des images et vision par ordinateur , 1995 .

[13]  Joachim Weickert,et al.  Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint , 2001, Journal of Mathematical Imaging and Vision.

[14]  Paolo Nesi,et al.  Variational approach to optical flow estimation managing discontinuities , 1993, Image Vis. Comput..

[15]  Gary J. Balas,et al.  Optical flow: a curve evolution approach , 1995, Proceedings., International Conference on Image Processing.

[16]  G. Aubert,et al.  A mathematical study of the relaxed optical flow problem in the space BV (&Ω) , 1999 .

[17]  P. Clarysse,et al.  Estimation du flot optique en présence de discontinuités : une revue , 1996 .

[18]  I. Cohen.  Nonlinear Variational Method for Optical Flow Computation , 2006 .

[19]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[20]  Georges Koepfler,et al.  Data fusion by segmentation. Application to texture discrimination , 1993 .