Authors:
Martin Schumann
;
Jan Hoppenheit
and
Stefan Müller
Affiliation:
University of Koblenz, Germany
Keyword(s):
Camera, Pose, Tracking, Model, Feature, Evaluation, Management.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Registration
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
Abstract:
Our tracking approach uses feature evaluation and management to estimate the camera pose on the camera image and a given geometric model. The aim is to gain a minimal but qualitative set of 2D image line and 3D model edge correspondences to improve accuracy and computation time. Reducing the amount of feature data makes it possible to use any complex model for tracking. Additionally, the presence of a 3D model delivers useful information to predict reliable features which can be matched in the camera image with high probability avoiding possible false matches. Therefore, a quality measure is defined to evaluate and select features best fitted for tracking upon criteria from rendering process and knowledge about the environment like geometry and topology, perspective projection, light and matching success feedback. We test the feature management to analyze the importance and influence of each quality criterion on the tracking and to find an optimal weighting.