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 [BibTeX] [Marc21]
An Implicit Motion Likelihood for Tracking with Particle Filters
Type of publication: Conference paper
Citation: odobez-bmvc-03
Booktitle: British Machine Vision Conference (BMVC)
Series: Lecture Notes in Computer Science
Year: 2003
Publisher: Springer Verlag
Address: Norwich, UK
Note: Similar to RR-03-15.
Crossref: odobez-rr-03-15:
Abstract: Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the data are correlated, and that modeling such dependency should improve tracking robustness. To take data correlation into account, we propose a new model which can be interpreted as introducing a likelihood on implicit motion measurements. The proposed model allows to filter out visual distractors when tracking objects with generic models based on shape or color distribution representations, as shown by the reported experiments.
Userfields: ipdinar={2003}, ipdmembership={vision}, ipdpriority={2},
Projects Idiap
Authors Odobez, Jean-Marc
Ba, Silèye O.
Gatica-Perez, Daniel
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Total mark: 0
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