Mörwald Thomas, Prankl Johann, Zillich Michael, Vincze Markus
Vienna University of Technology, Gusshausstr. 25-29, 1040 Vienna, Austria.
J Real Time Image Process. 2015;10(4):683-697. doi: 10.1007/s11554-013-0388-4. Epub 2013 Dec 20.
The huge amount of literature on real-time object tracking continuously reports good results with respect to accuracy and robustness. However, when it comes to the applicability of these approaches to real-world problems, often no clear statements about the tracking situation can be made. This paper addresses this issue and relies on three novel extensions to Monte Carlo particle filtering. The first, , together with the second, , leads to faster convergence and a more accurate pose estimation. The third, removes jitter and ensures convergence. These extensions significantly increase robustness and accuracy, and further provide a basis for an algorithm we found to be essential for tracking systems performing in the real world: . Relying on the extensions above, it reports qualitative states of tracking as follows. indicates if the pose has already been found. gives a statement about the confidence of the currently tracked pose. detects when the algorithm fails. determines the degree of occlusion if only parts of the object are visible. Building on tracking state detection, a scheme is proposed as a measure of which views of the object have already been learned and which areas require further inspection. To the best of our knowledge, this is the first tracking system that explicitly addresses the issue of estimating the tracking state. Our open-source framework is available online, serving as an easy-access interface for usage in practice.
大量关于实时目标跟踪的文献不断报道在准确性和鲁棒性方面取得的良好成果。然而，当涉及到这些方法在实际问题中的适用性时，对于跟踪情况往往无法做出明确的说明。本文解决了这个问题，并依赖于对蒙特卡洛粒子滤波的三个新颖扩展。第一个扩展，与第二个扩展一起，实现了更快的收敛和更精确的姿态估计。第三个扩展消除了抖动并确保了收敛。这些扩展显著提高了鲁棒性和准确性，并进一步为我们发现对于在现实世界中运行的跟踪系统至关重要的一种算法提供了基础：。基于上述扩展，它报告跟踪的定性状态如下。表示姿态是否已被找到。对当前跟踪姿态的置信度给出说明。检测算法何时失败。如果只有物体的部分可见，则确定遮挡程度。基于跟踪状态检测，提出了一种方案，作为衡量物体的哪些视图已经被学习以及哪些区域需要进一步检查的一种方法。据我们所知，这是第一个明确解决估计跟踪状态问题的跟踪系统。我们的开源框架可在线获取，作为在实践中使用的便捷接口。
