文章基本信息

标题：Estimate 3D Arm Motion with Hierarchical Limb Model
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作者：Xuesong Yu ; Jiafeng Liu
期刊名称：Computer and Information Science
印刷版ISSN：1913-8989
电子版ISSN：1913-8997
出版年度：2009
卷号：2
期号：1
页码：156
DOI：10.5539/cis.v2n1P156
出版社：Canadian Center of Science and Education
摘要：Normal 0 7.8 ? 0 2 false false false MicrosoftInternetExplorer4  /* Style Definitions */ table.MsoNormalTable {mso-style-name:????; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} Focusing on the problem of low computation efficiency in the process of tracking human 3D motion, an algorithm for Estimating 3D arm motion with Hierarchy Limb Model (HLM) is proposed. In our algorithm, the Hierarchy Limb Model (HLM) is proposed based on the human 3D skeleton model. Facilitated by graph decomposition, the arm motion state space, modeled by Hierarchy Limb Model (HLM), can be discomposed into low dimension subspaces. The Top-Down search strategy and the Particle Filter are used to tracking the arm motion, thus the amount of particle in tracking can be reduced. To handle server self-occlusions, the weighted color histogram and image contour are used to modeling the observation likelihood function. The result of experiment shows that our algorithm can advance the computation efficiency and handle effectively self-occlusions.