CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Real-time Hybrid Tracking using Edge and Texture Information
International Journal of Robotics Research
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour/texture approach for visual tracking
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Three things everyone should know to improve object retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ORB: An efficient alternative to SIFT or SURF
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Robust real time object pose tracking is an essential component for robotic applications as well as for the growing field of augmented reality. Currently available systems are typically either optimized for textured objects or for uniformly colored objects. The proposed approach combines complementary interest points in a common tracking framework which allows to handle a broad variety of objects regardless of their appearance and shape. A thorough evaluation of state of the art interest points shows that a multi scale FAST detector in combination with our own image descriptor outperforms all other combinations. Additionally, we show that a combination of complementary features improves the tracking performance slightly further.