Visual hand tracking using nonparametric sequential belief propagation

Wei Liang; Yunde Jia; Cheng Ge

doi:10.1007/11538059_71

Visual hand tracking using nonparametric sequential belief propagation

Wei Liang^*, Yunde Jia, Cheng Ge

^*Corresponding author for this work

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	679-687
Number of pages	9
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3644
Issue number	PART I
DOIs	https://doi.org/10.1007/11538059_71
Publication status	Published - 2005
Event	International Conference on Intelligent Computing, ICIC 2005 - Hefei, China Duration: 23 Aug 2005 → 26 Aug 2005

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10.1007/11538059_71

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title = "Visual hand tracking using nonparametric sequential belief propagation",

abstract = "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.",

author = "Wei Liang and Yunde Jia and Cheng Ge",

year = "2005",

doi = "10.1007/11538059_71",

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AU - Liang, Wei

AU - Jia, Yunde

AU - Ge, Cheng

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AB - 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.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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IS - PART I

T2 - International Conference on Intelligent Computing, ICIC 2005

Y2 - 23 August 2005 through 26 August 2005

ER -

Visual hand tracking using nonparametric sequential belief propagation

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