An Implicit Motion Likelihood for Tracking with Particle Filters

• Martigny, Switzerland: IDIAP, 2003

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.

Keywords: vision ; odobez

Note:

Published in British Machine Vision Conference (BMVC), Norwich, 2003.

Reference

• EPFL-REPORT-82931
• URL: http://publications.idiap.ch/downloads/reports/2003/rr03-15.pdf

Record created on 2006-03-10, modified on 2017-05-10