Robust tracking of spatial related components

This paper introduces a hierarchical approach for multi-component tracking, where the object-to-be-tracked is modeled as a group of spatial related parts. We propose to use a robust particle filtering framework for tracking the individual components and outline how the spatial coherency between the parts can be efficiently integrated by analyzing a two-level hierarchy of particle filters. Including spatial information allows to handle common tracking problems like occlusions, clutter or blur. Furthermore, the dynamic calculation of particle set uncertainties allows a dynamic adaption of stiffness values for the spatial model to e. g. force occluded parts to stay in spatial relation. The experimental section proves the robustness of the proposed tracker on challenging sequences of the VIVID-PETS database.