Learning a deep network with spherical part model for 3D hand pose estimation
3D hand pose estimation is a hot research topic in recent years. It's been widely used in many advanced applications for virtual reality and human computer interaction, since it provides a natural interface for communication between human and cyberspace. Despite the fast development of this field, it is still a difficult task due to the various challenges. In this paper, we aim to build a 3D hand pose estimation system which can correctly detect human hands and accurately estimate its pose using depth images. To guarantee the robustness of our system, we design a hand model called spherical part model (SPM) and train a deep convolutional neural network using this model. Moreover, to reduce the influence of human's omissions, we use a data driven approach to integrate them together. Our network can more accurately estimate hand pose based on prior knowledge of human hand. To demonstrate the superiority of our method, a complete experiment is conducted on two public and one self built datasets. The results show that our system can detect human hands with average precision at almost 90% and the average error distance of the pose estimation is about 10 millimeters, and is better than the other state of the art works.
2017 IEEE International Conference on Robotics and Automation (ICRA)