M. Rätsch, C. Blumer (Switzerland), G. Teschke (Germany), and T. Vetter (Switzerland)
Cascaded Condensation Tracking, Wavelet Approximated Reduced Vector Machine, Coarse-to-Fine Particle Filter, Condensation, Multi-Object Face Tracking, Active Dual Camera System
The Condensation and the Wavelet Approximated Reduced Vector Machine (W-RVM) approach are joined by the core idea to spend only as much as necessary effort for easy to discriminate regions (Condensation) and measurement locations (W-RVM) of the feature space, but most for regions and locations with high statistical likelihood to contain the object of interest. We unify both approaches by adapting the W-RVM classifier to tracking and refine the Condensation approach. Additionally, we utilize Condensation for abstract multi-dimensional feature vectors and provide a template based tracking of the three-dimensional camera scene. Moreover, we introduce a robust multi-object tracking by extensions to the Condensation approach. The new 3D Cascaded Condensation Tracking (CCT) for multiple objects yields a more than 10 times faster tracking than state-of-art detection methods. In our experiments we compare different tracking approaches using an active dual camera system for face tracking.
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