State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture

Abstract

In this study, we propose a method for minimizing the noise of Kinect sensors for 3D skeleton estimation. Notably, it is difficult to effectively remove nonlinear noise when estimating 3D skeleton posture; however, the proposed randomized unscented Kalman filter reduces the nonlinear temporal noise effectively through the state estimation process. The 3D skeleton data can then be estimated at each step by iteratively passing the posterior state during the propagation and updating process. Ultimately, the performance of the proposed method for 3D skeleton estimation is observed to be superior to that of conventional methods based on experimental results.