High-Accuracy Real-Time Whole-Body Human Motion Tracking Based on Constrained Nonlinear Kalman Filtering

Abstract

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.