Fast online human pose estimation via 3D voxel data

In this paper, a novel approach is proposed to recover human body pose from 3D voxel data. 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 robustness towards voxel noise. The main concept of our approach is based on an example based approach. Human posture candidates are constructed beforehand from a large motion capture database, and the most appropriate posture is estimated per frame by comparing the likelihoods between 3D voxel data and posture candidates. The evaluation is formulated by introducing a histogram- based feature vector that represents the 3D shape context of human body. In addition, a fast near-neighbor search metric is installed prior to the evaluation process, in order to reduce the computational cost and ensure real-time processing. Estimation stability is also improved by a graphical model of motion, which adds a smoothing effect to the motion sequence. We demonstrate the effectiveness of our approach with experiments on both synthetic and real image sequences.