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Proceedings Paper

A kinematic model for Bayesian tracking of cyclic human motion
Author(s): Thomas Greif; Rainer Lienhart
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Paper Abstract

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.

Paper Details

Date Published: 18 January 2010
PDF: 10 pages
Proc. SPIE 7543, Visual Information Processing and Communication, 75430K (18 January 2010); doi: 10.1117/12.838788
Show Author Affiliations
Thomas Greif, Univ. of Augsburg (Germany)
Rainer Lienhart, Univ. of Augsburg (Germany)

Published in SPIE Proceedings Vol. 7543:
Visual Information Processing and Communication
Amir Said; Onur G. Guleryuz, Editor(s)

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