A kinematic model for Bayesian tracking of cyclic human motion

  • We introduce a two-dimensional kinematic model for cyclic motions of humans, which is suitable for the use as temporal prior in any Bayesian tracking framework. This human motion model is solely based on simple kinematic properties: the joint accelerations. Distributions of joint accelerations subject to the cycle progress are learned from training data. We present results obtained by applying the introduced model to the cyclic motion of backstroke swimming in a Kalman filter framework that represents the posterior distribution by a Gaussian. We experimentally evaluate the sensitivity of the motion model with respect to the frequency and noise level of assumed appearance-based pose measurements by simulating various fidelities of the pose measurements using ground truth data.

Download full text files

Export metadata


Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Author:Thomas GreifGND, Rainer LienhartGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/1313
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2009-16)
Publishing Institution:Universität Augsburg
Release Date:2009/10/20
Tag:motion model; Bayesian tracking; pose estimation; swim motion analysis
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht