eprintid: 5946
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/59/46
datestamp: 2023-11-09 16:17:41
lastmod: 2023-11-09 16:17:41
status_changed: 2023-11-09 16:04:19
type: article
metadata_visibility: show
creators_name: Saini, S.
creators_name: Zakaria, N.
creators_name: Rambli, D.R.A.
creators_name: Sulaiman, S.
title: Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization
ispublished: pub
keywords: algorithm; annealed particle filter; Article; controlled study; hierarchical multi swarm cooperative particle swarm optimization; hierarchical particle swarm optimization; image analysis; intermethod comparison; markerless articulated human motion tracking; mathematical analysis; measurement accuracy; motion analysis system; nonlinear system; process optimization; algorithm; human; movement (physiology); physiology; theoretical model, Algorithms; Humans; Models, Theoretical; Movement
note: cited By 17
abstract: The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims. © 2015 Saini et al.
date: 2015
publisher: Public Library of Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929305746&doi=10.1371%2fjournal.pone.0127833&partnerID=40&md5=8b5ae31942e9fad4f93fc36bdb6ce3a3
id_number: 10.1371/journal.pone.0127833
full_text_status: none
publication: PLoS ONE
volume: 10
number: 5
refereed: TRUE
issn: 19326203
citation:   Saini, S. and Zakaria, N. and Rambli, D.R.A. and Sulaiman, S.  (2015) Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.  PLoS ONE, 10 (5).   ISSN 19326203