CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Hand Tracking with Flocks of Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Pose tracking from natural features on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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With the 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 that 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 article, 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.