Authors:
Vijay John
;
Spela Ivekovic
and
Emanuele Trucco
Affiliation:
University of Dundee, United Kingdom
Keyword(s):
Articulated human motion tracking, Hierarchical particle swarm optimisation, Annealed particle filter.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Tracking of People and Surveillance
Abstract:
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 co
nsistency is better than PF and compares favourably with the APF, outperforming it in sequences with sudden and fast motion.
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