Filtrage conditionnel pour la trajectographie dans des sequences d'images - Application au suivi de points

Abstract : In this paper, we propose a new conditional formulation of classical filtering methods dedicated to image sequence based tracking. These conditional filters allow to consider a state model and a measure model which both depend on the image sequence data. On this basis, we derive two filters for the point tracking problem, which authorize to cope with trajectories exhibiting abrupt changes and occlusions. They combine a dynamic relying on the optical flow constraint and measures provided by a matching technique. The first tracker is linear, well-suited to image sequences exhibiting global dominant motion. This filter is deduced through the use of a new estimator called the conditional linear minimum variance estimator. The second one is a nonlinear tracker, implemented from a particle filter. This latter allows to track points whose motion may only be locally described.
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https://hal.inria.fr/inria-00306731
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Submitted on : Wednesday, May 25, 2011 - 2:56:43 PM
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  • HAL Id : inria-00306731, version 1

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Elise Arnaud, Etienne Mémin, Bruno Cernuschi-Frias. Filtrage conditionnel pour la trajectographie dans des sequences d'images - Application au suivi de points. 14e congrès francophone de Reconnaissance des formes et d'Intelligence artificielle (RFIA '04), Jan 2004, Toulouse, France. ⟨inria-00306731⟩

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