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Math. Comput. Appl. 2016, 21(3), 37;

Fuzzy Grey Prediction-Based Particle Filter for Object Tracking

College of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
College of Mathematics and Finance, Hunan University of Humanities, Science and Technology, Loudi 417000, Hunan, China
Author to whom correspondence should be addressed.
Academic Editor: Saeid Abbasbandy
Received: 4 June 2016 / Revised: 25 July 2016 / Accepted: 16 August 2016 / Published: 23 August 2016
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A particle filter is a powerful tool for object tracking based on sequential Monte Carlo methods under a Bayesian estimation framework. A major challenge for a particle filter in object tracking is how to allocate particles to a high-probability density area. A particle filter does not take into account the historical prior information on the generation of the proposal distribution and, thus, it cannot approximate posterior density well. Therefore, a new fuzzy grey prediction-based particle filter (called FuzzyGP-PF) for object tracking is proposed in this paper. First, a new prediction model which was based on fuzzy mathematics theory and grey system theory was established, coined the Fuzzy-Grey-Prediction (FGP) model. Then, the history state sequence is utilized as prior information to predict and sample a part of particles for generating the proposal distribution in the particle filter. Simulations are conducted in the context of two typical maneuvering motion scenarios and the results indicate that the proposed FuzzyGP-PF algorithm can exhibit better overall performance in object tracking. View Full-Text
Keywords: fuzzy grey prediction; particle filter; object tracking; historical prior information fuzzy grey prediction; particle filter; object tracking; historical prior information

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Yang, L.; Lu, Z. Fuzzy Grey Prediction-Based Particle Filter for Object Tracking. Math. Comput. Appl. 2016, 21, 37.

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