Matching-based algorithms have been commonly used in planar object tracking. They often model a planar object as a set of keypoints, and then find correspondences between keypoint sets via descriptor matching. In previous work, unary constraints on appearances or locations are usually used to guide the matching. However, these approaches rarely utilize structure information of the object, and are thus suffering from various perturbation factors. In this paper, we proposed a graph-based tracker, named Gracker, which is able to fully explore the structure information of the object to enhance tracking performance. We model a planar object as a graph, instead of a simple collection of keypoints, to represent its structure. Then, we reformulate tracking as a sequential graph matching process, which establishes keypoint correspondence in a geometric graph matching manner. For evaluation, we compare the proposed Gracker with state-of-the-art planar object trackers on three benchmark datasets: two public ones and a newly collected one. Experimental results show that Gracker achieves robust tracking results against various environmental variations, and outperforms other algorithms in general on the datasets.

中文翻译：

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Gracker：基于图的平面对象跟踪器


基于匹配的算法已普遍用于平面对象跟踪。他们通常将平面对象建模为一组关键点，然后通过描述符匹配找到关键点集之间的对应关系。在之前的工作中，通常使用外观或位置的一元约束来指导匹配。然而，这些方法很少利用对象的结构信息，因此受到各种扰动因素的影响。在本文中，我们提出了一种基于图的跟踪器，名为 Gracker，它能够充分探索对象的结构信息以增强跟踪性能。我们将平面对象建模为图形，而不是简单的关键点集合，以表示其结构。然后，我们将跟踪重新表述为顺序图匹配过程，以几何图匹配方式建立关键点对应。为了进行评估，我们在三个基准数据集（两个公共数据集和一个新收集的数据集）上将所提出的 Gracker 与最先进的平面对象跟踪器进行了比较。实验结果表明，Gracker 针对各种环境变化实现了稳健的跟踪结果，并且在数据集上总体优于其他算法。