Three Dimensional Monocular Human Motion Analysis in End-Effector Space

Søren Hauberg, Jérôe Lapuyade, Morten Engell-Nørregård, Kenny Erleben, Kim Steenstrup Pedersen

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.