Coarse-to-fine particle filters for multi-object human computer interaction
Matthias Rätsch, Clemens Blumer, Gerd Teschke, Thomas R. Vetter · 2009
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.