Linear and Quadratic Subsets for Template-Based Tracking

Selim Benhimane, A. Ladikos, V. Lepetit, Nassir Navab
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引用次数: 30

Abstract

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 textured-ness. 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.
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基于模板的跟踪的线性和二次子集
我们提出了一种在收敛区域大小和计算时间方面显著提高基于模板的匹配性能的方法。这是通过选择模板的一个子集来完成的,该子集验证了关于运动参数的线性或二次性的假设(在优化期间进行的)。我们称这些子集为线性子集或二次子集。虽然已经提出了子集选择方法,但它们通常不试图提供线性或二次子集,而是依赖于纹理性等启发式方法。由于对最优子集的简单搜索会导致大型模板的组合爆炸,因此我们提出了一种简单的算法,它不以最优子集为目标,而是以低成本提供非常好的线性或二次子集,即使对于大型模板也是如此。仿真和真实序列的实验结果表明,该方法与现有的子集选择方法相比具有优越性。
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