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Authors: Séverine Dubuisson 1 ; Myriam Robert-Seidowsky 2 and Jonathan Fabrizio 2

Affiliations: 1 CNRS, UMR 7222 and ISIR, France ; 2 LRDE-EPITA, France

ISBN: 978-989-758-091-8

Keyword(s): Visual Tracking, Particle Filter, Likelihood Function, Correction Step.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Tracking and Visual Navigation

Abstract: The particle filter is known to be efficient for visual tracking. However, its parameters are empirically fixed, depending on the target application, the video sequences and the context. In this paper, we introduce a new algorithm which automatically adjusts online two majors of them: the correction and the propagation parameters. Our purpose is to determine, for each frame of a video, the optimal value of the correction parameter and to adjust the propagation one to improve the tracking performance. On one hand, our experimental results show that the common settings of particle filter are sub-optimal. On another hand, we prove that our approach achieves a lower tracking error without needing to tune these parameters. Our adaptive method allows to track objects in complex conditions (illumination changes, cluttered background, etc.) without adding any computational cost compared to the common usage with fixed parameters.

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Paper citation in several formats:
Dubuisson, S.; Robert-Seidowsky, M. and Fabrizio, J. (2015). A Self-adaptive Likelihood Function for Tracking with Particle Filter.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 446-453. DOI: 10.5220/0005260004460453

@conference{visapp15,
author={Séverine Dubuisson. and Myriam Robert{-}Seidowsky. and Jonathan Fabrizio.},
title={A Self-adaptive Likelihood Function for Tracking with Particle Filter},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={446-453},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005260004460453},
isbn={978-989-758-091-8},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - A Self-adaptive Likelihood Function for Tracking with Particle Filter
SN - 978-989-758-091-8
AU - Dubuisson, S.
AU - Robert-Seidowsky, M.
AU - Fabrizio, J.
PY - 2015
SP - 446
EP - 453
DO - 10.5220/0005260004460453

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