An Implicit Motion Likelihood for Tracking with Particle Filters

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.


Publié dans:
British Machine Vision Conference (BMVC)
Présenté à:
British Machine Vision Conference (BMVC)
Année
2003
Publisher:
Norwich, UK, Springer Verlag
Mots-clefs:
Note:
Similar to RR-03-15.
Laboratoires:




 Notice créée le 2006-03-10, modifiée le 2019-12-05

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