Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32109
Title: Optimized particles for 3-D tracking
Authors: Chen, H
Li, Y
Keywords: 3-D tracking
Effective sampling size
Importance density function
Particle filter
Issue Date: 2010
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2010, v. 6424 lnai, no. part 1, p. 749-761 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: 3-D visual tracking is useful for many of its applications. In this paper, we propose two different ways for different system configurations to optimize particle filter for enhancing 3-D tracking performances. On one hand, a new data fusion method is proposed to obtain the optimal importance density function for active vision systems. On the other hand, we develop a method for reconfigurable vision systems to maximize the effective sampling size in particle filter, which consequentially helps to solve the degeneracy problem and minimize the tracking error.
Description: 3rd International Conference on Intelligent Robotics and Applications, ICIRA 2010, Shanghai, 10-12 November 2010
URI: http://hdl.handle.net/10397/32109
ISBN: 3642165834
9783642165832
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-16584-9_71
Appears in Collections:Conference Paper

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