|   | 
Author Daniel Ponsa; Antonio Lopez
Title Variance reduction techniques in particle-based visual contour Tracking Type Journal Article
Year 2009 Publication Pattern Recognition Abbreviated Journal (up) PR
Volume 42 Issue 11 Pages 2372–2391
Keywords Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling
Abstract This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done.
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ PoL2009a Serial 1168
Permanent link to this record