Random Forest Classifiers for REAL-TIME optical markerless tracking
Autores: Iñigo Barandiaran and Charlotte Cottez and Céline Paloc and Manuel Graña
Fecha: 22.01.2008
Abstract
Augmented reality (AR) is a very promising technology that can be applied in many areas such as healthcare, broadcasting or manufacturing industries. One of the bottlenecks of such application is a robust real-time optical markerless tracking strategy. In this paper we focus on the development of tracking by detection for plane homography estimation. Feature or keypoint matching is a critical task in such approach. We propose to apply machine learning techniques to solve this problem. We present an evaluation of an optical tracking implementation based on Random Forest classifier. The implementation has been successfully applied to indoor and outdoor augmented reality design review application.
BIB_text
@Article {
author = {Iñigo Barandiaran and Charlotte Cottez and Céline Paloc and Manuel Graña},
title = {Random Forest Classifiers for REAL-TIME optical markerless tracking},
pages = {559-562},
volume = {2},
abstract = {
Augmented reality (AR) is a very promising technology that can be applied in many areas such as healthcare, broadcasting or manufacturing industries. One of the bottlenecks of such application is a robust real-time optical markerless tracking strategy. In this paper we focus on the development of tracking by detection for plane homography estimation. Feature or keypoint matching is a critical task in such approach. We propose to apply machine learning techniques to solve this problem. We present an evaluation of an optical tracking implementation based on Random Forest classifier. The implementation has been successfully applied to indoor and outdoor augmented reality design review application.
}
date = {2008-01-22},
year = {2008},
}
author = {Iñigo Barandiaran and Charlotte Cottez and Céline Paloc and Manuel Graña},
title = {Random Forest Classifiers for REAL-TIME optical markerless tracking},
pages = {559-562},
volume = {2},
abstract = {
Augmented reality (AR) is a very promising technology that can be applied in many areas such as healthcare, broadcasting or manufacturing industries. One of the bottlenecks of such application is a robust real-time optical markerless tracking strategy. In this paper we focus on the development of tracking by detection for plane homography estimation. Feature or keypoint matching is a critical task in such approach. We propose to apply machine learning techniques to solve this problem. We present an evaluation of an optical tracking implementation based on Random Forest classifier. The implementation has been successfully applied to indoor and outdoor augmented reality design review application.
}
date = {2008-01-22},
year = {2008},
}
