In this paper, we address the problem of detection and tracking of group and individual targets. In particular, we focus on a group model with a virtual leader which models the bulk or group parameter. To perform the sequential inference, we propose a Markov Chain Monte Carlo (MCMC)-based Particle algorithm with a marginalisation scheme using pairwise Kalman filters. Numerical simulations illustrate the ability of the algorithm to detect and track targets within groups, as well as infer both the correct group structure and the number of targets over time.
Tracking of coordinated groups using marginalised MCMC-based Particle algorithm
2009-03-01
252225 byte
Conference paper
Electronic Resource
English
An improvement on an MCMC-based video tracking algorithm
British Library Online Contents | 2015
|MCMC-Particle-based group tracking of space objects within Bayesian framework
Online Contents | 2014
|MCMC PARTICLE FILTER-BASED VEHICLE TRACKING METHOD USING MULTIPLE HYPOTHESES AND APPEARANCE MODEL
British Library Conference Proceedings | 2013
|