  We present a new online approach to track human whole-body motion from motion
capture data, i.e., positions of labeled markers attached to the human body.
Tracking in noisy data can be effectively performed with the aid of
well-established recursive state estimation techniques. This allows us to
systematically take noise of the marker measurements into account. However, as
joint limits imposed by the human body have to be satisfied during estimation,
first we transform this constrained estimation problem into an unconstrained
one by using periodic functions. Then, we apply the Smart Sampling Kalman
Filter to solve this unconstrained estimation problem. The proposed recursive
state estimation approach makes the human motion tracking very robust to
partial occlusion of markers and avoids any special treatment or reconstruction
of the missed markers. A concrete implementation built on the kinematic human
reference model of the Master Motor Map framework and a Vicon motion capture
system is evaluated. Different captured motions show that our implementation
can accurately estimate whole-body human motion in real-time and outperforms
existing gradient-based approaches. In addition, we demonstrate its ability to
smoothly handle incomplete marker data.
