A Covariance Approximation on Euclidean Space for Visual Tracking
Stefan Kluckner, Thomas Mauthner, Horst Bischof · 2009
This work proposes an efficient approximation of a covariance based feature representation for tracking. In contrast to approximated similarity measurements between second order moments, such as the Foerstner metric, we propose to approximate single distributions by specified sampling. We derive an efficient and discriminative feature representation that allows to compute distances between covariance-based descriptors on Euclidean space. This approximated representation fits perfectly to the application of tracking, where efficient similarity measurement significantly controls the efficiency and the real-time capability of the resulting approach. Furthermore, we highlight the advantages of the proposed approximation for learning an object-specific representation during tracking. The experimental evaluation shows results on standard tracking videos and compares our derived approach to state-of-the-art methods based on other covariance representations. 1