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A novel 3-D motion estimation approach to virtual viewpoint control

The novelty of this paper is the introduction of the interacting multiple model probabilistic data association filter (IMMPDAF) to the pose tracking problem. The interacting multiple model (IMM) technique allows the existence of more than one dynamic system in the filtering process and in return leads to improved accuracy and stability even under abrupt motion changes. The probabilistic data association (PDA) framework makes the automatic selection of measurement sets possible, resulting in enhanced robustness to occlusions and moving objects. As the PDA associates stereo correspondences probabilistically, the explicit establishment of stereo matches is not necessary except during initialization, and the point features presence in the outer region of the stereo image pair can be utilized. The performance is demonstrated by applying the pose information to control cameras in a virtual environment.