Combining Model Matching with Feature Tracking for 3D Human Upper Body Pose Recovering

Recovering 3D human pose from monocular video sequences is a challenging problem due to the highly articulated structure and inherent depth ambiguity.By take fully advantage of the both benefits of feature tracking and model matching,a new method of markerless 3D human upper body pose tracking is presented.First,an effective object function is derived from the matched SIFT correspondences based on the length invariability constraint.Then,the global pose is obtained by an iterative optimization process.The pose of other joints is initially estimated by inverse kinematics,then,testified by model matching.A local search strategy is followed to estimate the optimal pose if the initial estimated pose is inaccurate.The experiment results show that the proposed algorithm can reconstruct 3D human upper body pose from monocular video sequences.