Feature management for efficient camera tracking

Authors:
Harald Wuest;Alain Pagani;Didier Stricker
Affiliations:
Centre for Advanced Media Technology, Nanyang Technological University, Singapore and Department of Virtual and Augmented Reality, Fraunhofer IGD, TU Darmstadt, GRIS, Germany;Department of Virtual and Augmented Reality, Fraunhofer IGD, TU Darmstadt, GRIS, Germany;Department of Virtual and Augmented Reality, Fraunhofer IGD, TU Darmstadt, GRIS, Germany
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

Citing 7
Cited 2

Marker-less Tracking for AR: A Learning-Based Approach

ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
Real-Time Simultaneous Localisation and Mapping with a Single Camera

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recursive Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Template Update Problem

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusing Points and Lines for High Performance Tracking

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Online camera pose estimation in partially known and dynamic scenes

ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Fusion of 3d and appearance models for fast object detection and pose estimation

ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II

Acquisition of High Quality Planar Patch Features

ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Mobile Augmented Reality: Composing the feature map retrieval process for robust and ready-to-use monocular tracking

Computers and Graphics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In dynamic scenes with occluding objects many features need to be tracked for a robust real-time camera pose estimation. An open problem is that tracking too many features has a negative effect on the real-time capability of a tracking approach. This paper proposes a method for the feature management which performs a statistical analysis of the ability to track a feature and then uses only those features which are very likely to be tracked from a current camera position. Thereby a large set of features in different scales is created, where every feature holds a probability distribution of camera positions from which the feature can be tracked successfully. As only the feature points with the highest probability are used in the tracking step, the method can handle a large amount of features in different scale without losing the ability of real time performance. Both the statistical analysis and the reconstruction of the features' 3D coordinates are performed online during the tracking and no preprocessing step is needed.