Articulated human motion tracking with HPSO. / John, Vijay; Ivekovic, Spela; Trucco, Emanuele.
Visapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, VOL 1. ed. / A Ranchordas; H Araujo. Setubal : Institute for Systems and Technologies of Information, Control and Communication, 2009. p. 531-538.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - CHAP
T1 - Articulated human motion tracking with HPSO
A1 - John,Vijay
A1 - Ivekovic,Spela
A1 - Trucco,Emanuele
AU - John,Vijay
AU - Ivekovic,Spela
AU - Trucco,Emanuele
PB - Institute for Systems and Technologies of Information, Control and Communication
CY - Setubal
PY - 2009
Y1 - 2009
N2 - <p>In this paper, we address full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult nonlinear optimisation problems. Our tracking approach is designed to address the limits of particle filtering approaches: it initialises automatically, removes the need for a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We quantitatively compare the performance of HPSO with that of the particle filter (PF) and annealed particle filter (APF). Our test results, obtained using the framework proposed by (Balan et al., 2005) to compare articulated body tracking algorithms, show that HPSO's pose estimation accuracy and consistency is better than PF and compares favourably with the APF, outperforming it in sequences with sudden and fast motion.</p>
AB - <p>In this paper, we address full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult nonlinear optimisation problems. Our tracking approach is designed to address the limits of particle filtering approaches: it initialises automatically, removes the need for a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We quantitatively compare the performance of HPSO with that of the particle filter (PF) and annealed particle filter (APF). Our test results, obtained using the framework proposed by (Balan et al., 2005) to compare articulated body tracking algorithms, show that HPSO's pose estimation accuracy and consistency is better than PF and compares favourably with the APF, outperforming it in sequences with sudden and fast motion.</p>
KW - Articulated human motion tracking
KW - Hierarchical particle swarm optimisation
KW - Annealed particle filter
KW - CAPTURE
M1 - Conference contribution
SN - 978-989-8111-69-2
BT - Visapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, VOL 1
T2 - Visapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, VOL 1
A2 - Araujo,H
ED - Araujo,H
SP - 531
EP - 538
ER -