In order to overcome typical problems in markerless motion capture from video, such as ambiguities, noise, and occlusions, many techniques reduce the high dimensional search space by integration of prior information about the movement pattern or scene. In this work, we present an approach in which geometric prior information about the floor location is integrated in the pose tracking process. We penalize poses in which body parts intersect the ground plane by employing soft constraints in the pose estimation framework. Experiments with rigid objects and the HumanEVA-Ⅱ benchmark show that tracking is remarkably stabilized.
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