3D pose estimation in high dimensional search spaces with local memorization

In this paper, a stochastic approach for extracting the articulated 3D human postures by synchronized multiple cameras in the high-dimensional configuration spaces is presented. Annealed Particle Filtering (APF) [1] seeks for the globally optimal solution of the likelihood. We improve and extend the APF with local memorization to estimate the suited kinematic postures for a volume sequence directly instead of projecting a rough simplified body model to 2D images. Our method guides the particles to the global optimization on the basis of local constraints. A segmentation algorithm is performed on the volumetric models and the process is repeated. We assign the articulated models 42 degrees of freedom. The matching error is about 6% on average while tracking the posture between two neighboring frames.