Real-time Optical Markerless Tracking for Augmented Reality Applications

Autores: Iñigo Barandiaran and Céline Paloc and Manuel Graña

Fecha: 01.06.2010

Journal of Real-Time Image Processing


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Abstract

Augmented Reality (AR) technology consists in adding computer-generated information (2D/3D) to a real video sequence in such a manner that the real and virtual objects appear coexisting in the same world. In order to get a realistic illusion, the real and virtual objects must be properly aligned with respect to each other, which requires a robust real-time tracking strategy - one of the bottlenecks of AR applications. In this paper we describe the limitations and advantages of different optical tracking technologies, and we present our customized implementation of both recursive tracking and tracking by detection approaches. The second approach requires the implementation of a classifier and we propose the use of a Random Forest classifier. We evaluated both approaches in the context of an AR application for design review. Some conclusions regarding the performance of each approach are given.

BIB_text

@Article {
author = {Iñigo Barandiaran and Céline Paloc and Manuel Graña},
title = {Real-time Optical Markerless Tracking for Augmented Reality Applications},
journal = {Journal of Real-Time Image Processing},
pages = {129-138},
volume = {5},
keywds = {
Augmented Reality, Optical Markerless tracking, Tracking by Detection
}
abstract = {
Augmented Reality (AR) technology consists in adding computer-generated information (2D/3D) to a real video sequence in such a manner that the real and virtual objects appear coexisting in the same world. In order to get a realistic illusion, the real and virtual objects must be properly aligned with respect to each other, which requires a robust real-time tracking strategy - one of the bottlenecks of AR applications. In this paper we describe the limitations and advantages of different optical tracking technologies, and we present our customized implementation of both recursive tracking and tracking by detection approaches. The second approach requires the implementation of a classifier and we propose the use of a Random Forest classifier. We evaluated both approaches in the context of an AR application for design review. Some conclusions regarding the performance of each approach are given.
}
isi = {1},
date = {2010-06-01},
year = {2010},
}
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