Three Dimensional Monocular Human Motion Analysis in End-Effector Space

Authors:
Søren Hauberg;Jerome Lapuyade;Morten Engell-Nørregård;Kenny Erleben;Kim Steenstrup Pedersen
Affiliations:
The eScience Center, Dept. of Computer Science, University of Copenhagen, Denmark;The eScience Center, Dept. of Computer Science, University of Copenhagen, Denmark;The eScience Center, Dept. of Computer Science, University of Copenhagen, Denmark;The eScience Center, Dept. of Computer Science, University of Copenhagen, Denmark;The eScience Center, Dept. of Computer Science, University of Copenhagen, Denmark
Venue:
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Year:
2009

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Abstract

In this paper, we present a novel approach to three dimensional human motion estimation from monocular video data. We employ a particle filter to perform the motion estimation. The novelty of the method lies in the choice of state space for the particle filter. Using a non-linear inverse kinematics solver allows us to perform the filtering in end-effector space. This effectively reduces the dimensionality of the state space while still allowing for the estimation of a large set of motions. Preliminary experiments with the strategy show good results compared to a full-pose tracker.