Markerless human articulated tracking using hierarchical particle swarm optimisation

In this paper, we address markerless full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional non-linear 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 non-linear optimisation problems. We show that a small number of particles achieves accuracy levels comparable with several recent algorithms. PSO initialises automatically, does not need a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We compare experimentally HPSO with particle filter (PF), annealed particle filter (APF) and partitioned sampling annealed particle filter (PSAPF) using the computational framework provided by Balan et al. HPSO accuracy and consistency are better than PF and compare favourably with those of APF and PSAPF, outperforming it in sequences with sudden and fast motion. We also report an extensive experimental study of HPSO over ranges of values of its parameters.