Variance reduction techniques in particle-based visual contour tracking

被引：8
|
作者：
Ponsa, Daniel [1 ]
Lopez, Antonio M. [1 ,2 ]
机构：
[1] Univ Autonoma Barcelona, Ctr Visio Computador, Bellaterra 08193, Spain
[2] Univ Autonoma Barcelona, Dept Ciencies Computacio, Bellaterra 08193, Spain
来源：
PATTERN RECOGNITION | 2009年 / 42卷 / 11期
关键词：
Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling; SHAPE; FILTERS; MOTION; FRAMEWORK; MODELS;
D O I：
10.1016/j.patcog.2009.04.007
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码：2372 / 2391
页数：20
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