Abstract

<!-- /* Font Definitions */ @font-face {font-family:??; panose-1:2 1 6 0 3 1 1 1 1 1; mso-font-alt:SimSun; mso-font-charset:134; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 135135232 16 0 262145 0;} @font-face {font-family:"\@??"; panose-1:2 1 6 0 3 1 1 1 1 1; mso-font-charset:134; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 135135232 16 0 262145 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; text-align:justify; text-justify:inter-ideograph; mso-pagination:none; font-size:10.5pt; mso-bidi-font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:??; mso-font-kerning:1.0pt;} p.MsoBodyText, li.MsoBodyText, div.MsoBodyText {margin-top:7.8pt; margin-right:0cm; margin-bottom:2.5pt; margin-left:0cm; mso-para-margin-top:.5gd; mso-para-margin-right:0cm; mso-para-margin-bottom:2.5pt; mso-para-margin-left:0cm; text-align:justify; text-justify:inter-ideograph; line-height:12.0pt; mso-line-height-rule:exactly; mso-pagination:none; font-size:10.0pt; mso-bidi-font-size:11.0pt; font-family:"Times New Roman"; mso-fareast-font-family:??; mso-font-kerning:1.0pt; mso-bidi-font-weight:bold;} /* Page Definitions */ @page {mso-page-border-surround-header:no; mso-page-border-surround-footer:no;} @page Section1 {size:612.0pt 792.0pt; margin:72.0pt 90.0pt 72.0pt 90.0pt; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} -->

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.

This work is licensed under a Creative Commons Attribution 4.0 License.