CUED Publications database

Multiple object tracking using evolutionary MCMC-based particle algorithms

Septier, F and Carmi, A and Pang, SK and Godsill, SJ (2009) Multiple object tracking using evolutionary MCMC-based particle algorithms. IFAC Proceedings Volumes (IFAC-PapersOnline), 15. pp. 798-803. ISSN 1474-6670

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Abstract

Algorithms are presented for detection and tracking of multiple clusters of co-ordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the filtering density of the clusters' state. The filters' tracking efficiency is enhanced by incorporating various sampling improvement strategies into the basic Metropolis-Hastings scheme. Thus, an evolutionary stage consisting of two primary steps is introduced: 1) producing a population of different chain realizations, and 2) exchanging genetic material between samples in this population. The performance of the resulting evolutionary filtering algorithms is demonstrated in two different settings. In the first, both group and target properties are estimated whereas in the second, which consists of a very large number of targets, only the clustering structure is maintained. © 2009 IFAC.

Item Type: Article
Uncontrolled Keywords: Estimation algorithms Genetic algorithms Monte Carlo method Multitarget tracking Recursive estimation
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 12:46
Last Modified: 08 Dec 2014 21:45
DOI: 10.3182/20090706-3-FR-2004.0367