Linear and Quadratic Subsets for Template-Based Tracking

We propose a method that dramatically improves the performance of template-based matching in terms of size of convergence region and computation time. This is done by selecting a subset of the template that verifies the assumption (made during optimization) of linearity or quadraticity with respect to the motion parameters. We call these subsets linear or quadratic subsets. While subset selection approaches have already been proposed, they generally do not attempt to provide linear or quadratic subsets and rely on heuristics such as texturedness. Because a naive search for the optimal subset would result in a combinatorial explosion for large templates, we propose a simple algorithm that does not aim for the optimal subset but provides a very good linear or quadratic subset at low cost, even for large templates. Simulation results and experiments with real sequences show the superiority of the proposed method compared to existing subset selection approaches.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Selim Benhimane, Alexander Ladikos, Vincent Lepetit, Nassir Navab
• Keywords: Computer Vision, CVPR 2007, Large Templates, Optimal Subset, Quadratic Subset, Quadratic Subsets, Subset Selection Approaches