Square binary patterns have become the de facto fiducial marker for most computer vision applications. Existing tracking solutions suffer a number of limitations, such as the low frame-rate and sensitivity to partial occlusions. This work aims at overcoming these limitations, by exploiting temporal information in video-sequences. We propose a parallel detection, compensation and tracking (PDCAT) framework, which can be integrated into any binary marker system. Our solution is capable of recovering markers even when they become mostly occluded. Furthermore, the low processing time of the tracking task makes PDCAT more than an order of magnitude faster than a track-by-detect solution. This is particularly important for embedded computer vision applications, wherein the detection run at a very low frame rate. In the experiments conducted on an embedded computer, the processing frame rate of the track-by-detect solution was merely 11 FPS. Our solution, on the other hand, was capable of processing more than 100 FPS.

方形二进制模式已成为大多数计算机视觉应用程序的事实上的基准标记。现有的跟踪解决方案受到许多限制，例如低帧速率和对部分遮挡的敏感性。这项工作旨在通过利用视频序列中的时间信息来克服这些限制。我们提出了一种并行检测，补偿和跟踪（PDCAT）框架，该框架可以集成到任何二进制标记系统中。我们的解决方案能够恢复标记，即使它们大部分被遮挡了。此外，跟踪任务的低处理时间使PDCAT比逐个跟踪解决方案快一个数量级。这对于嵌入式计算机视觉应用特别重要，其中检测以非常低的帧速率运行。在嵌入式计算机上进行的实验中，逐条检测解决方案的处理帧速率仅为11 FPS。另一方面，我们的解决方案能够处理超过100 FPS。