Coarse-to-fine particle filters for multi-object human computer interaction

Efficient motion tracking of faces is an important aspect for human computer interaction (HCI). In this paper we combine the condensation and the wavelet approximated reduced vector machine (W-RVM) approach. Both are joined by the core idea to spend only as much as necessary effort for easy to discriminate regions (Condensation) or vectors (W-RVM) of the feature space, but most for regions with high statistical likelihood to contain objects of interest. We adapt the W-RVM classifler for tracking by providing a probabilistic output. In this paper we utilize condensation for template based tracking of the three-dimensional camera scene. Moreover, we introduce a robust multi-object tracking by extensions to the condensation approach. The novel coarse-to-flne condensation yields a more than 10 times faster tracking than state-of-art detection methods. We demonstrate more natural HCI applications by high resolution face tracking within a large camera scene with an active dual camera system.