Multiple view human articulated tracking using charting and particle swarm optimisation

We present a framework for markerless articulated human motion tracking in multi-view sequences. We learn motion models of common actions in a low-dimensional latent space using charting, a nonlinear dimensionality reduction tool which estimates automatically the dimension of the latent space and keeps similar poses close together in it. Additionally charting obtains the inverse mapping from the lowdimensional latent space to the high-dimensional joint angle space. The tracking is formulated as a low-dimensional nonlinear optimisation in the latent space 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. Tracking results with the walking, kicking, praying, posing and punch sequences demonstrate the good accuracy and performance of our approach.