Markerless human motion capture using hierarchical particle swarm optimisation

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), annealed particle filter (APF) and partitioned sampling annealed particle filter (PSAPF). Our test results, obtained using the framework proposed by Balan et al [1] to compare articulated body tracking algorithms, show that HPSO's pose estimation accuracy and consistency is better than PF, APF and PSAPF.