Variational Space-Time Motion Segmentation

Authors:
Daniel Cremers;Stefano Soatto
Affiliations:
-;-
Venue:
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Year:
2003

Citing 0
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Abstract

We propose a variational method for segmenting imagesequences into spatio-temporal domains of homogeneousmotion. To this end, we formulate the problem of motionestimation in the framework of Bayesian inference, using aprior which favors domain boundaries of minimal surfacearea. We derive a cost functional which depends on a surfacein space-time separating a set of motion regions, aswell as a set of vectors modeling the motion in each region.We propose a multiphase level set formulation of thisfunctional, in which the surface and the motion regions arerepresented implicitly by a vector-valued level set function.Joint minimization of the proposed functional results in aneigenvalue problem for the motion model of each region andin a gradient descent evolution for the separating interface.Numerical results on real-world sequences demonstratethat minimization of a single cost functional generates asegmentation of space-time into multiple motion regions.