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Ass. Lect. Amr Mohamed Abdelhameed Nagy Abdo :: Publications:

Hybrid Iterated Kalman Particle Filter for Object Tracking Problems
Authors: 1Amr M. Nagy, 2Ali Ahmed and 1Hala H. Zayed
Year: 2013
Keywords: Kalman Filter, Particle Filter, Nonlinear/Non-Gaussian, Object Tracking.
Journal: in IProceedings of the 8 International Conference on Computer Vision Theory and Applications, 2013
Volume: 2
Issue: 1
Pages: 375-381
Publisher: VISAPP
Local/International: International
Paper Link:
Full paper Amr Mohamed Abdelhameed Nagy Abdo_VISAPP_2013_46_CR.pdf
Supplementary materials Not Available

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.

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