Hybrid Iterated Kalman Particle Filter for Object Tracking Problems

Amr M. Nagy, Ali Ahmed, Hala H. Zayed

2013

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 experiment results.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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