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A robust stochastic filter for point tracking in image sequences

Élise Arnaud 1 Etienne Mémin 1 Bruno Cernuschi-Frias 1, 2
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The approach we investigate for point tracking combines within a stochastic filtering framework a dynamic model relying on the optical flow constraint and measurements provided by a matching technique. Focusing on points belonging to regions described by a global dominant motion, the proposed tracking system is linear. Since we focus on the case where the system depends on the images, the tracker is built from a Conditional Linear Filter, derived through the use of a conditional linear minimum variance estimator. This conditional tracker authorizes to significantly improve results in some general situation. In particular, such an approach allows us to deal in a simple way with the tracking of points following trajectories with abrupt changes and occlusions.
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Submitted on : Tuesday, April 21, 2009 - 1:41:00 PM
Last modification on : Monday, February 7, 2022 - 1:54:01 PM
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  • HAL Id : inria-00306728, version 1


Élise Arnaud, Etienne Mémin, Bruno Cernuschi-Frias. A robust stochastic filter for point tracking in image sequences. IEEE Asian conference on computer vision, 2004, ile de Jeju, South Korea. ⟨inria-00306728⟩



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