Object Tracking and Pose Estimation Using Light-Field Object Models

Geometric object models have been widely used for visual object tracking. In this contribution we present particle filter based object tracking with pose estimation using an appearance based lightfield object model. A light-field is an image-based object representation which can be used to render a photo realistic view of an arbitrarily shaped object from arbitrary viewpoints. It is shown how lightfield object models can be generated and utilized. Furthermore, we show how these models fit into the probabilistic framework of dynamic state estimation by defining an appropriate likelihood distribution from an image similarity metric. Finally, we present results and accuracy evaluations from tracking experiments of different objects.