RANDOM FOREST CLASSIFIERS FOR REAL-TIME OPTICAL MARKERLESS TRACKING

Iñigo Barandiaran, Charlotte Cottez, Céline Paloc, Manuel Graña

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.

References

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Paper Citation


in Harvard Style

Barandiaran I., Cottez C., Paloc C. and Graña M. (2008). RANDOM FOREST CLASSIFIERS FOR REAL-TIME OPTICAL MARKERLESS TRACKING . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 559-564. DOI: 10.5220/0001086405590564


in Bibtex Style

@conference{visapp08,
author={Iñigo Barandiaran and Charlotte Cottez and Céline Paloc and Manuel Graña},
title={RANDOM FOREST CLASSIFIERS FOR REAL-TIME OPTICAL MARKERLESS TRACKING},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={559-564},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001086405590564},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - RANDOM FOREST CLASSIFIERS FOR REAL-TIME OPTICAL MARKERLESS TRACKING
SN - 978-989-8111-21-0
AU - Barandiaran I.
AU - Cottez C.
AU - Paloc C.
AU - Graña M.
PY - 2008
SP - 559
EP - 564
DO - 10.5220/0001086405590564