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. © 2009 Springer.
CITATION STYLE
Hauberg, S., Lapuyade, J., Engell-Nørregård, M., Erleben, K., & Steenstrup Pedersen, K. (2009). Three dimensional monocular human motion analysis in end-effector space. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5681 LNCS, pp. 235–248). https://doi.org/10.1007/978-3-642-03641-5_18
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