Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Keypoint Signatures for Fast Learning and Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Improving the Agility of Keyframe-Based SLAM
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time and robust monocular SLAM using predictive multi-resolution descriptors
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
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Real-time camera tracking in previously unknown scene is attractive to a wide spectrum of computer vision applications. In Recent years, Simultaneous Localization and Mapping (SLAM) system and its varieties have shown extraordinary camera tracking performance. However, the robustness of these systems to rapid and erratic camera motion is still limited because of the typically used Local Localization scheme. To overcome this limitation, we present an efficient online camera tracking algorithm using a Global Localization scheme which matches features in a global way through two steps: First, coarse matches are obtained through nearest feature descriptor search. Afterwards, a Game Theoretic approach is exploited to eliminate the incorrect matches and the left correct matches can be used to estimate the camera pose. Result shows our camera tracking algorithm has significantly improved the robustness of camera tracking system to rapid and erratic camera motion.