3D voxel based online human pose estimation via robust and efficient hashing

In this paper, we present a novel framework to recover human body pose on multi camera systems. Our framework leverages 3D voxel data, which are reconstructed from multi-camera systems. The use of voxel data leads to viewpoint-free estimation, which benefits in that reconstruction of a training model is needless in different multi-camera arrangements. Other notable aspects of our approach are real-time ensuring speed (up to 30 fps), flexibility towards various complex motions and environments. We treat the pose estimation problem as estimating human pose label from the voxel features and tackle this by example based approach. To ensure the real-time speed and to improve precision of pose estimation, a newly fast and robust near-neighbor search metric is installed prior to the evaluation process, what we call CSI-PSH. We demonstrate the effectiveness of our approach with experiments on both synthetic and real image sequences.