Pose tracking from natural features on mobile phones

In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art f...

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Bibliographic Details
Published in:2008 7th IEEE International Symposium on Mixed and Augmented Reality pp. 125 - 134
Main Authors: Wagner, Daniel, Reitmayr, Gerhard, Mulloni, Alessandro, Drummond, Tom, Schmalstieg, Dieter
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 01.09.2008
IEEE
Series:ACM Other Conferences
Subjects:
and virtual realities
Applied computing > Computers in other domains > Personal computers and PC applications
augmented
Cameras
Computational modeling
Computing methodologies > Artificial intelligence > Computer vision
Computing methodologies > Artificial intelligence > Computer vision > Computer vision problems
Computing methodologies > Artificial intelligence > Computer vision > Computer vision problems > Tracking
Computing methodologies > Artificial intelligence > Computer vision > Computer vision tasks
Computing methodologies > Artificial intelligence > Computer vision > Computer vision tasks > Scene understanding
Computing methodologies > Computer graphics > Graphics systems and interfaces > Virtual reality
Computing methodologies > Machine learning
Detectors
Feature extraction
H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems - Artificial
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Mixed > augmented reality
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Virtual reality
I.4.8 [Image Processing and Computer Vision]: Scene Analysis - Tracking
Mobile handsets
mobile phones
natural features
pose tracking
Real time systems
Target tracking
ISBN:9781424428403, 1424428408
Online Access:Get full text
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Summary:In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. We present evaluations on robustness and performance on various devices and finally discuss their appropriateness for Augmented Reality applications.
ISBN:9781424428403
1424428408
DOI:10.1109/ISMAR.2008.4637338