Register based on efficient scene learning and keypoint matching for Augmented Reality system

Register is steadily gaining in importance due to the drive from various computer vision applications, such as Augmented Reality (AR), mobile computing, and human-machine interface. Efficient keypoint-based approachs are widely used in scene register. This paper focuses on designing a robust and flexible registration method for wide-area augmented reality applications using scene recognition and natural features tracking techniques. A linear structured SVM classifier is used to perform scene learning online, which allows us to quickly adapt our model to a given environment. And the hybrid tracking strategy is implemented by combining both wide and narrow baseline techniques. The experiments have been conducted to demonstrate the validity of our methods.