Controlling Virtual Cameras Based on a Robust Model-Free Pose Acquisition Technique

This paper presents a novel method that acquires camera position and orientation from a stereo image sequence without prior knowledge of the scene. To make the algorithm robust, the interacting multiple model probabilistic data association filter (IMMPDAF) is introduced. 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. In addition to the IMMPDAF, the trifocal tensor is employed in the computation so that the step of reconstructing the 3-D models can be eliminated. This further guarantees the precision of estimation and computation efficiency. Real stereo image sequences have been used to test the proposed method in the experiment. The recovered 3-D motions are accurate in comparison with the ground truth data and have been applied to control cameras in a virtual environment.