Representing cyclic human motion using functional analysis

Dirk Ormoneit

doi:10.1016/j.imavis.2005.09.004

2005-12-01

DOI：10.1016/j.imavis.2005.09.004

原文链接

查看原文

摘要

En 中文

We present a robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data. The pose of the body is represented by a time-series of joint angles which are automatically segmented into a sequence of motion cycles. The mean and the principal components of these cycles are computed using a new algorithm that enforces smooth transitions between the cycles by operating in the Fourier domain. Key to this method is its ability to automatically deal with noise and missing data. A learned walking model is then exploited for Bayesian tracking of 3D human motion. (C) 2005 Elsevier B.V. All rights reserved.

Keyword:

human motion

functional data analysis

missing data

singular value decomposition

principal component analysis

motion capture

tracking

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Image and Vision Computing

IF:

4.2

论文数:

4.1K

被引数:

6.7K

机构

暂无机构信息

引用论文

A Looped-Bowtie RFID Tag Antenna Design for Metallic Objects

IEEE Transactions on Antennas and Propagation

IF0

2013-02-01

PREAI

Ken-Huang Lin; Sung-Lin Chen; Raj Mittra

Nonstatistical fragmentation of large molecules

Physical Review A

IF0

2014-03-06

PREAI

M. H. Stockett; H. Zettergren; L. Adoui; J. D. Alexander; U. Bērziņš; T. Chen; M. Gatchell; N. Haag; B. A. Huber; P. Hvelplund; A. Johansson; H. A. B. Johansson; K. Kulyk; S. Rosén; P. Rousseau; K. Støchkel; H. T. Schmidt; H. Cederquist

Abrasion and breakage phenomena in agitated crystal suspensions

Chemical Engineering Science

IF0

1992-08-01

PREAI

Barbara Mazzarotta

Tracking disease progression by searching paths in a temporal network of biological processes

PLOS ONE

IF0

2017-04-27

OAAI

Rajat Anand; Samrat Chatterjee