Particle filter-based camera tracker fusing marker and feature point cues
Particle filter-based camera tracker fusing marker and feature point cues
复制标题
基于粒子滤波器的相机跟踪器融合标记和特征点线索
DOI：
10.1117/12.703150
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
T. Ebrahimi
中科院分区：
文献类型：
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作者：
David Marimón;Y. Maret;Y. Abdeljaoued;T. Ebrahimi
本文提出了一种基于视频的摄像机跟踪器，它结合了基于标记和基于特征点的线索在粒子滤波框架。该框架依赖于它们的互补性能。一方面，当参考（标记）可用时，基于标记的跟踪器可以鲁棒地恢复相机位置和方向，但一旦参考不可用，则会失败。另一方面，使用特征点线索的基于滤波器的相机跟踪器仍然可以提供给定先前状态的预测估计。然而，跟踪器倾向于漂移，并且当参考信号再次出现时通常无法恢复。因此，我们提出了一种融合，其中过滤器的估计根据每个线索的单独测量值进行更新。融合滤波器的特殊性在于在一个框架中处理不同种类的线索。该框架保持单个运动模型，并且其预测每次由一个线索校正。更确切地说，当参考可用时选择基于标记的提示，而否则选择基于特征点的提示。通过在两个不同的似然分布之间切换来更新滤波器的状态。每个似然分布适应于测量的类型（线索）。对真实的案例的评估表明，这两种方法的融合优于单独的跟踪结果。
This paper presents a video-based camera tracker that combines marker-based and feature point-based cues within a particle filter framework. The framework relies on their complementary performances. On the one hand, marker-based trackers can robustly recover camera position and orientation when a reference (marker) is available but fail once the reference becomes unavailable. On the other hand, filter-based camera trackers using feature point cues can still provide predicted estimates given the previous state. However, the trackers tend to drift and usually fail to recover when the reference reappears. Therefore, we propose a fusion where the estimate of the filter is updated from the individual measurements of each cue. The particularity of the fusion filter is to manipulate different sorts of cues in a single framework. The framework keeps a single motion model and its prediction is corrected by one cue at a time. More precisely, the marker-based cue is selected when the reference is available whereas the feature point-based cue is selected otherwise. The filter's state is updated by switching between two different likelihood distributions. Each likelihood distribution is adapted to the type of measurement (cue). Evaluations on real cases show that the fusion of these two approaches outperforms the individual tracking results.