Markerless Planar Tracking in Augmented Reality Using Geometric Structure

This paper presents a novel tracking method based on Kanade-Lucas-Tomasi(KLT) for Markerless Augmented Reality system. Two main contributions are listed as follow: 1) feature points are tracked in a multi-level image pyramid model and the tracked points on different levels are blended together; 2) tracked points are refined using the geometric structure between them, including wiping off fictitious points and recovering missing points. In addition, it adapts to acute scale variation and poor quality image tracking. Experimental results show that the proposed algorithm is robust to scale invariant and the re-projection error of feature points on each frame is smaller than one pixel, which is kept at sub-pixel.