Tracking of 3D multi-part objects using multiple viewpoint time-varying sequences

Presents a shape and pose estimation method for 3D multi-part objects, the purpose of which is to easily map objects from the real world into virtual environments. In general, complex 3D multi-part objects cause unwanted self-occlusion and non-rigid motion. To deal with the problem, we employ multiple viewpoint time-varying sequences, since there is enough information to estimate the parameters in the sensory data. In our framework, to minimize the error between the selected image feature points and the estimated model parameters, we employ a model fitting procedure which can adaptively select corresponding pairs. We have demonstrated that our system works well both for single part objects and for multiple-part objects using real image data