Abstract
Most of the work in head-pose tracking has concentrated on single-camera systems with a relatively small field of view which have limited accuracy because features are only observed in a single viewing direction. We present a multi-camera pose tracker that handles an arbitrary configuration of cameras rigidly fixed to the observer's head. By using multiple cameras, we increase the robustness and accuracy by which a 6-DOF pose is tracked. However, in a multi-camera rig setting, earlier methods for determining the unknown pose from three world-to-camera correspondences are no longer applicable. We present a RANSAC [Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography] based method that handles multi-camera rigs by using a fast non-linear minimization step in each RANSAC round.
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