Title: Object tracking using the particle filter optimised by the improved artificial fish swarm algorithm

Authors: Zhigao Zeng; Haixing Bao; Zhiqiang Wen; Wenqiu Zhu

Addresses: College of Computer, Hunan University of Technology, Hunan 412000, China; Intelligent Information Perception and Processing Technology, Hunan Province Key Laboratory, China ' College of Computer, Hunan University of Technology, Hunan 412000, China; Intelligent Information Perception and Processing Technology, Hunan Province Key Laboratory, China ' College of Computer, Hunan University of Technology, Hunan 412000, China; Intelligent Information Perception and Processing Technology, Hunan Province Key Laboratory, China ' College of Computer, Hunan University of Technology, Hunan 412000, China; Intelligent Information Perception and Processing Technology, Hunan Province Key Laboratory, China

Abstract: In particle filter algorithm, the weight values of particles will gradually decrease as the increase of iteration times and the variance of the weight value of the particles will increase. This will lead to an increase in the deviation between the estimated state and the true state. In order to deal with this problem, an improved particle filter algorithm is proposed in this paper. That is, an improved artificial fish swarm optimisation algorithm is used to optimise the traditional particle filter. In the improved particle filter algorithm, the resampled particles will be driven to the region with high likelihood function to increase the weight values of the particles. Thus, the estimated state is closer to the real state. Experiment results show the advantage of our new algorithm over a range of existing algorithms.

Keywords: object tracking; particle filter; artificial fish swarm algorithm; global search.

DOI: 10.1504/IJIIDS.2019.102323

International Journal of Intelligent Information and Database Systems, 2019 Vol.12 No.1/2, pp.6 - 19

Received: 06 Jul 2018
Accepted: 06 Nov 2018
Published online: 18 Sep 2019 *

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