Articulated Models from Video

Abstract

Past research on model-based tracking of articulated targets has neglected to address the problems of model-acquisition and initialization. However, for model-based approaches to ever become practical and autonomous, these important issues need to be addressed Towards this goal, this paper-presents a model-acquisition framework for acquiring articulated models directly from monocular video. Both structure, shape, and appearance of articulated models are estimated In addition, the initialization problem is solved by estimating pose information for at least one frame of a sequence, allowing subsequent model-based tracking. The presented work is based on basic assumptions and hence not restricted towards specific types of targets. It has in particular the ability to process human as well as non-human targets and makes no assumptions with respect to the structure of the kinematic tree or complexity. This work hence presents a set of systematic solutions to the problems of model-acquisition and initialization that bridge the gap between state of the art model-based tracking approaches and practical applications.