1Efficient Matchings and Mobile Augmented Reality

With fast growing popularity of smart phones in recent years, augmented reality (AR) on mobile devices is gaining more attention and becomes more demanding than ever before. However, the limited processors in mobile devices are not quite promising for AR applications which require real-time processing speed. The challenge exists due to the fact that while fast features are usually not robust enough in matchings, robust features like SIFT or SURF are not computationally efficient. There is always a tradeoff between robustness and efficiency and it seems that we have to sacrifice one for the other. While this is true for most existing features, researchers have been working on designing new features with both robustness and efficiency. In this paper, we are not trying to present a completely new feature. Instead, we propose an efficient matching method for robust features. An adaptive scoring scheme and a more distinctive descriptor are also proposed for performance improvements. Besides, we have developed an outdoor augmented reality system that is based on our proposed methods. The system demonstrates that not only it can achieve robust matchings efficiently, it is also capable to handle large occlusions such as passengers and moving vehicles, which is another challenge for many AR applications