Human upper-body motion capturing using Kinect
Wei-Chia Kao, Shih-Chung Hsu, Chung-Lin Huang · 2014
This paper proposes a real-time upper human motion capturing method to estimate the positions of upper limb joints by using Kinect. For human articulated motion capturing, the body part self-occlusion is a nontrivial problem. The system consists of hybrid action type recognition, body part segmentation, and offset compensation. The hybrid action type classifier consists of Adaboost and Random Forest classifier. The major contributions of this paper are offset compensation and self-occluded joint recovery. The offset is the difference between the output and the ground truth. The offset compensation is proposed by correcting the estimated locations of the joints. For different user action type, we train an appropriate offset classifier for offset compensation. Finally, we propose a postprocessing to justify the effectiveness of the offset compensation.