Mean-shift-FAST algorithm to handle motion-blur with tracking fiducial markers

Abstract

Vision-based registration methods for augmented reality systems recently have been the subject of intensive research due to their potential to accurately align virtual objects with the real world. The drawbacks of these vision-based approaches, however, are their high computational cost and lack of robustness. Motion blur and partial occlusion are considered two of the most critical problems that affect robustness of tracking fiducial markers, which is used in many vision-based tracking methods like augmented reality. To overcome these two problems, this paper presents a novel method which merges FAST detection with mean shift tracking algorithms. The original color-based mean shift tracking has a major problem of detecting fiducial markers. Therefore, we used “keypoints” feature to make them more distinguishable. These keypoints are detected by FAST corner detector and tracked by mean shift tracker. Experiments show that the proposed algorithm is able to handle problems of motion blur and partial occlusion efficiently.