Dense multi-frame optic flow for non-rigid objects using subspace constraints

Ravi Garg, Luis Pizarro, Daniel Rueckert, Lourdes Agapito

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations


In this paper we describe a variational approach to computing dense optic flow in the case of non-rigid motion. We optimise a global energy to compute the optic flow between each image in a sequence and a reference frame simultaneously. Our approach is based on subspace constraints which allow to express the optic flow at each pixel in a compact way as a linear combination of a 2D motion basis that can be pre-estimated from a set of reliable 2D tracks. We reformulate the multi-frame optic flow problem as the estimation of the coefficients that multiplied with the known basis will give the displacement vectors for each pixel. We adopt a variational framework in which we optimise a non-linearised global brightness constancy to cope with large displacements and impose homogeneous regularization on the multi-frame motion basis coefficients. Our approach has two strengths. First, the dramatic reduction in the number of variables to be computed (typically one order of magnitude) which has obvious computational advantages and second, the ability to deal with large displacements due to strong deformations. We conduct experiments on various sequences of non-rigid objects which show that our approach provides results comparable to state of the art variational multi-frame optic flow methods.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages14
EditionPART 4
StatePublished - 2011
Externally publishedYes
Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: 8 Nov 201012 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume6495 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th Asian Conference on Computer Vision, ACCV 2010
Country/TerritoryNew Zealand


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