Visual Hand Tracking Using Nonparametric Sequential Belief Propagation

Hand tracking is a challenging problem due to the complexity of searching in a 20+ degrees of freedom space for an optimal estimate. This paper develops a statistical method for robust visual hand tracking, in which graphical model decoupling different hand joints is performed to represent the hand...

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Bibliographic Details
Published inAdvances in Intelligent Computing pp. 679 - 687
Main Authors Liang, Wei, Jia, Yunde, Ge, Cheng
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Belief Propagation
Computer science; control theory; systems
Exact sciences and technology
Graphical Model
Hand Joint
Hand Tracking
Particle Filter
Pattern recognition. Digital image processing. Computational geometry
Belief
Statistical analysis
Decoupling
Tracking
Probabilistic approach
Motion estimation
Image processing
Inference
Artificial intelligence
Modeling
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Summary:Hand tracking is a challenging problem due to the complexity of searching in a 20+ degrees of freedom space for an optimal estimate. This paper develops a statistical method for robust visual hand tracking, in which graphical model decoupling different hand joints is performed to represent the hand constraints. Each node of the graphical model represents the position and the orientation of each hand joint in world coordinate. Then, the problem of hand tracking is transformed into an inference of graphical model. We extend Nonparametric Belief Propagation to a sequential process to track hand motion. The Experiment results show that this approach is robust for 3D hand motion tracking.
ISBN:3540282262
9783540282266
ISSN:0302-9743
1611-3349
DOI:10.1007/11538059_71