Fast 3D reconstruction of human shape and motion tracking by parallel fast level set method

Yumi Iwashita, Ryo Kurazume, Kenji Hara, Seiichi Uchida, Ken'ichi Morooka, Tsutomu Hasegawa

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

5 被引用数 (Scopus)

抄録

This paper presents a parallel algorithm of the Level Set Method named the Parallel Fast Level Set Method, and its application for real-time 3D reconstruction of human shape and motion. The Fast Level Set Method is an efficient implementation algorithm of the Level Set Method and has been applied to several applications such as object tracking in video images and 3D shape reconstruction using multiple stereo cameras. In this paper, we implement the Fast Level Set Method on a PC cluster and develop a real-time motion capture system for arbitrary viewpoint image synthesis. To obtain high performance on a PC cluster, efficient load-balancing and resource allocation algorithms are crucial problems. We develop a novel optimization technique of load distribution based on the estimation of moving direction of object boundaries. In this technique, the boundary motion is estimated in the framework of the Fast Level Set Method, and the optimum load distribution is predicted and performed according to the estimated boundary motion and the current load balance. Experiments of human shape reconstruction and arbitrary viewpoint image synthesis using the proposed system are successfully carried out.

本文言語英語
ホスト出版物のタイトル2008 IEEE International Conference on Robotics and Automation, ICRA 2008
ページ980-986
ページ数7
DOI
出版ステータス出版済み - 2008
イベント2008 IEEE International Conference on Robotics and Automation, ICRA 2008 - Pasadena, CA, 米国
継続期間: 5 19 20085 23 2008

出版物シリーズ

名前Proceedings - IEEE International Conference on Robotics and Automation
ISSN(印刷版)1050-4729

その他

その他2008 IEEE International Conference on Robotics and Automation, ICRA 2008
国/地域米国
CityPasadena, CA
Period5/19/085/23/08

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 制御およびシステム工学
  • 人工知能
  • 電子工学および電気工学

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