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Robust 2 D Tracking for Real-Time Augmented Reality

Vision-based registration techniques for augmented reality systems have been the subject of intensive research recently due to their potential to accurately align virtual objects with the real world. The downfall of these vision-based approaches, however, is their high computational cost and lack of robustness. This paper describes the implementation of a fast, but accurate, vision-based corner tracker that forms the basis of a pattern-based augmented reality system. The tracker predicts corner positions by computing a homography between known corner positions on a planar pattern and potential planar regions in a video sequence. Local search windows are then placed around these predicted locations in order to find the actual subpixel corner positions. Experimental results show the robustness of the corner tracking system with respect to occlusion, scale, orientation, and lighting.