This paper presents an evolutionary approach to estimating upper-body posture from multi-view markerless sequences. We fit a 24-dof skeleton model to sparse 3-D stereo data from an array of cameras. We use a particle swarm optimization algorithm which is intrinsically parallel, can incorporate constraints and does not require motion models. We subdivide the high-dimensional search space based on limb dynamics from application sequences and perform hierarchical fitting from the least to the most uncertain body parts. We show experimentally the advantages of this scheme against non-hierarchical optimization in terms of sharper error decrease. We report results with 3-D scanner data of a model human and noisy, calibrated stereo disparity maps of a real videoconferencing scene.
REF use PSO to fit a stick model to sparse 3D stereo data reconstructed from an array of cameras.
Human body posture via hierarchical evolutionary optimization
IN: BMVC06. (2006) III:999
Computer Science