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Authors: Amr M. Nagy 1 ; Ali Ahmed 2 and Hala H. Zayed 1

Affiliations: 1 Benha University, Egypt ; 2 Menofia University, Egypt

ISBN: 978-989-8565-48-8

Keyword(s): Kalman Filter, Particle Filter, Nonlinear/Non-Gaussian, Object Tracking.

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

Abstract: Particle Filters (PFs), are widely used where the system is non Linear and non Gaussian. Choosing the importance proposal distribution is a key issue for solving nonlinear filtering problems. Practical object tracking problems encourage researchers to design better candidate for proposal distribution in order to gain better performance. In this correspondence, a new algorithm referred to as the hybrid iterated Kalman particle filter (HIKPF) is proposed. The proposed algorithm is developed from unscented Kalman filter (UKF) and iterated extended Kalman filter (IEKF) to generate the proposal distribution, which lead to an efficient use of the latest observations and generates more close approximation of the posterior probability density. Comparing with previously suggested methods(e.g PF, PF-EKF, PF-UKF, PF-IEKF), our proposed method shows a better performance and tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation and exp eriment results. (More)

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Paper citation in several formats:
Nagy, A.; Ahmed, A. and H. Zayed, H. (2013). Hybrid Iterated Kalman Particle Filter for Object Tracking Problems.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 375-381. DOI: 10.5220/0004211403750381

@conference{visapp13,
author={Amr M. Nagy. and Ali Ahmed. and Hala H. Zayed.},
title={Hybrid Iterated Kalman Particle Filter for Object Tracking Problems},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={375-381},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004211403750381},
isbn={978-989-8565-48-8},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Hybrid Iterated Kalman Particle Filter for Object Tracking Problems
SN - 978-989-8565-48-8
AU - Nagy, A.
AU - Ahmed, A.
AU - H. Zayed, H.
PY - 2013
SP - 375
EP - 381
DO - 10.5220/0004211403750381

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