Model-based 3D human motion tracking and voxel reconstruction from sparse views

Abstract

This paper addresses a method for 3D human motion tracking and voxel-based reconstruction from sparse views. We adopt the annealed Gaussian based particle swarm optimization (AGPSO) for 3D human motion tracking. The AGPSO algorithm incorporates the temporal continuity information into the traditional particle swarm optimization (PSO) algorithm under a Bayesian framework. In the online tracking process, the state variables are estimated via the particle filtering, where the observation is designed as a minimized Markov Random Field (MRF) energy. Finally, voxel reconstruction is conducted using the skeleton shape prior via dynamic graph cut. The experimental results show that our method performs promisingly against the cluttered background and generates plausible voxel reconstructions from sparse views.