Human Motion Enhancement via Tobit Particle Filtering and Differential Evolution
Le Zhou, Nate Lannan, Guoliang Fan · 2021
This paper proposes a novel approach to improve the quality of human motion data captured by a depth sensor. Depth-based motion capture (D-Mocap) data often suffer significant errors due to noise, self-occlusion, interference, and other algorithmic limitations. We aim to improve 3D joint trajectories to be more kinematically admissible and anthropometrically consistent. The Tobit model is incorporated with a particle filter (TPF) to handle censored measurements. We also embed the DE algorithm in the TPF, which allows particles to be re-distributed and re-weighted according to bone length consistency before re-sampling. This integration leads to a new TPF-DE algorithm that harmoniously takes advantage of kinematic and anthropometric constraints. We compare our methods with several nonlinear Kalman filters and deep learning-based methods to demonstrate the efficacy of TPF-DE on both simulated and real-world D-Mocap data.