Abstract We propose a new non-rigid articulated point set registration framework for human pose estimation that aims at improving two recent registration techniques and filling the gap between the two. One is Coherent Point Drift (CPD) that is a powerful Gaussian Mixture Model (GMM)-based non-rigid registration method, but may not be suitable for articulated deformations due to the violation of motion coherence assumption. The other is articulated ICP (AICP) that is effective for human pose estimation but prone to be trapped in local minima without good correspondence initialization. To bridge the gap of the two, a new non-rigid registration method, called Global-Local Topology Preservation (GLTP), is proposed by integrating a Local Linear Embedding (LLE)-based topology constraint with CPD in a GMM
A non-rigid articulated point set registration framework for human pose estimation was developed by Song and Guoliang REF .
Non-rigid Articulated Point Set Registration for Human Pose Estimation
2015 IEEE Winter Conference on Applications of Computer Vision
2015 IEEE Winter Conference on Applications of Computer Vision
Computer Science