Identifying motion capture tracking markers with self-organizing maps

Matthias Weber, Heni Ben Amor, Thomas Alexander

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Motion Capture (MoCap) describes methods and technologies for the detection and measurement of human motion in all its intricacies. Most systems use markers to track points on a body. Especially with natural human motion data captured with passive systems (to not hinder the participant) deficiencies like low accuracy of tracked points or even occluded markers might occur. Additionally, such MoCap data is often unlabeled. In consequence, the system does not provide information about which body landmarks the points belong to. Self-organizing neural networks, especially selforganizing maps (SOMs), are capable of dealing with such problems. This work describes a method to model, initialize and train such SOMs to track and label potentially noisy motion capture data.

Original languageEnglish (US)
Title of host publicationIEEE Virtual Reality 2008, VR
Pages297-298
Number of pages2
DOIs
StatePublished - 2008
Externally publishedYes
EventIEEE Virtual Reality 2008, VR - Reno, NV, United States
Duration: Mar 8 2008Mar 12 2008

Publication series

NameProceedings - IEEE Virtual Reality

Other

OtherIEEE Virtual Reality 2008, VR
Country/TerritoryUnited States
CityReno, NV
Period3/8/083/12/08

Keywords

  • Connectionism and neural nets
  • H. 1.2 [models and principles]: User/machine systems
  • Human information processing; 1.2.6 [artificial intelligence]: Learning

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint

Dive into the research topics of 'Identifying motion capture tracking markers with self-organizing maps'. Together they form a unique fingerprint.

Cite this