ENERGY MINIMIZATION APPROACH FOR ONLINE DATA ASSOCIATION WITH MISSING DATA

Abir El Abed, Séverine Dubuisson, Dominique Béréziat

Abstract

Data association problem is of crucial importance to improve online target tracking performance in many difficult visual environments. Usually, association effectiveness is based on prior information and observation category. However, some problems can arise when targets are quite similar. Therefore, neither the color nor the shape could be helpful informations to achieve the task of data association. Likewise, problems can also arise when tracking deformable targets, under the constraint of missing data, with complex motions. Such restriction, i.e. the lack in prior information, limit the association performance. To remedy, we propose a novel method for data association, inspired from the evolution of the target dynamic model, and based on a global minimization of an energy vector. The main idea is to measure the absolute geometric accuracy between features. Its parameterless constitutes the main advantage of our energy minimization approach. Only one information, the position, is used as input to our algorithm. We have tested our approach on several sequences to show its effectiveness.

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Paper Citation


in Harvard Style

El Abed A., Dubuisson S. and Béréziat D. (2007). ENERGY MINIMIZATION APPROACH FOR ONLINE DATA ASSOCIATION WITH MISSING DATA . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 978-972-8865-74-0, pages 371-378. DOI: 10.5220/0002049003710378


in Bibtex Style

@conference{visapp07,
author={Abir El Abed and Séverine Dubuisson and Dominique Béréziat},
title={ENERGY MINIMIZATION APPROACH FOR ONLINE DATA ASSOCIATION WITH MISSING DATA},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2007},
pages={371-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002049003710378},
isbn={978-972-8865-74-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - ENERGY MINIMIZATION APPROACH FOR ONLINE DATA ASSOCIATION WITH MISSING DATA
SN - 978-972-8865-74-0
AU - El Abed A.
AU - Dubuisson S.
AU - Béréziat D.
PY - 2007
SP - 371
EP - 378
DO - 10.5220/0002049003710378