Human Pose Estimation in 3-D Space Using Adaptive Control Law With Point-Cloud-Based Limb Regression Approach

This paper presents the algorithm of human pose estimation in 3-D space using adaptive control law with point-cloud-based limb regression approach. The proposed approach is a data-driven method for 3-D pose estimation of human. In addition, we exploit the inverse relationship between the estimated parameter and the pose of limb, proposing a hybrid scheme, the combination of indirect adaptive scheme in Cartesian coordinate, and indirect adaptive scheme in spherical space. The experimental results show the ability of error tolerance, even dealing with sparse, partial, and noisy limb data. The comparison of the ground truth is provided with the standard dataset. The computation speed is sufficient, as it is expected to be used in real-time applications or as the 3-D feature in other recognition frameworks. Finally, the proposed algorithm is demonstrated with the point cloud sensing in real scene and the experimental results are included in this paper.