18 January 2010 A kinematic model for Bayesian tracking of cyclic human motion
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Proceedings Volume 7543, Visual Information Processing and Communication; 75430K (2010); doi: 10.1117/12.838788
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States
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.
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Thomas Greif, Rainer Lienhart, "A kinematic model for Bayesian tracking of cyclic human motion", Proc. SPIE 7543, Visual Information Processing and Communication, 75430K (18 January 2010); doi: 10.1117/12.838788; https://doi.org/10.1117/12.838788
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