Recognition and estimation of human locomotion with Hidden Markov Models
Gespeichert in:
| Deutscher übersetzter Titel: | Erfassung und Beurteilung menschlicher Bewegung mit Hilfe des Hidden Markov Models |
|---|---|
| Autor: | Fischer, Andreas; Gehrig, Dirk; Stein, Thorsten; Schwameder, Hermann; Schultz, Tanja |
| Erschienen in: | (Scientific) Proceedings of the 27th International Conference on Biomechanics in Sports (2009) : August 17 – 21 2009, University of Limerick - Ireland [27 International Conference on Biomechanics in Sports (2009) : Limerick, Ireland, August 17 – 21, 2009] |
| Veröffentlicht: | Limerick: 2009, S. 112-113, Lit. |
| Herausgeber: | Universität Konstanz ; University of Limerick / Physical Education and Sport Sciences Department |
| Format: | Literatur (SPOLIT) |
| Publikationstyp: | Sammelwerksbeitrag |
| Medienart: | Elektronische Ressource (online) Gedruckte Ressource |
| Sprache: | Englisch |
| Schlagworte: | |
| Online Zugang: | |
| Erfassungsnummer: | PU201110008853 |
| Quelle: | BISp |
Abstract des Autors
The Collaborative Research Centre “Humanoid Robots” situated at the University of Karlsruhe is aimed to construct a learning and cooperating service robot. To cope with its tasks it is necessary that the robot is able to identify diverse objects as well as different persons. Looking at stochastic models for pattern recognition Hidden Markov Models (HMMs) are described to be most suitable to classify time arranged data (Bilmes 2002). The objective of this study is to screen if the HMMs supply satisfying rates of recognition of human trajectory and angle data. Verf.-Referat