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.
Type de document :
Communication dans un congrès
IEEE Asian conference on computer vision, 2004, ile de Jeju, South Korea. 2004
https://hal.inria.fr/inria-00306728
Contributeur : Elise Arnaud
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Soumis le : mardi 21 avril 2009 - 13:41:00
Dernière modification le : vendredi 16 novembre 2018 - 01:30:39
Document(s) archivé(s) le : samedi 26 novembre 2016 - 00:34:23
Elise Arnaud, Etienne Memin, 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. 2004. 〈inria-00306728〉
