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| 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 | 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. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ PoL2009a | Serial | 1168 | ||
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