Document Type : Research Paper
Msc. student, Electrical and Computer Engineering, Semnan University
Electrical and Computer Engineering Department, Semnan University
There are challenges such as depth perception and self-occlusion, in the field of 3D human pose estimation and reconstruction which obstructs precise estimation of body joints. In this paper, we first extract human pose by focusing on 2D ground-truth using sparse coding and. In the second approach, we use a learning-based Convolutional Neural Networks using sparse coding and a model based rectifier to extract the estimated pose. Pose estimation by proposedmethod has reduced the mean error of the reconstruction in comparison with the state of the artworks.