论文标题
第一人称视野在对象跟踪方面有挑战吗？
Is First Person Vision Challenging for Object Tracking?
论文作者
论文摘要
在第一人称愿景（FPV）中，了解人类对象相互作用至关重要。跟踪遵循相机佩戴者操纵的对象的算法可以提供有效建模此类相互作用的有用线索。尽管以前有几次尝试在FPV应用程序中利用跟踪器，但仍缺少对该域中最先进的视觉跟踪器性能的有条理分析。在这篇简短的论文中，我们提供了FPV中对象跟踪的首次系统研究。我们的工作广泛分析了最近和基线FPV跟踪器在不同方面的性能。这是通过Trek-150实现的，Trek-150是一种新型的基准数据集，由150个密集注释的视频序列组成。结果表明，应该将更多的研究工作用于此问题，以便跟踪可以使FPV任务受益。本文的完整版本可从Arxiv：2108.13665获得。
Understanding human-object interactions is fundamental in First Person Vision (FPV). Tracking algorithms which follow the objects manipulated by the camera wearer can provide useful cues to effectively model such interactions. Despite a few previous attempts to exploit trackers in FPV applications, a methodical analysis of the performance of state-of-the-art visual trackers in this domain is still missing. In this short paper, we provide a recap of the first systematic study of object tracking in FPV. Our work extensively analyses the performance of recent and baseline FPV trackers with respect to different aspects. This is achieved through TREK-150, a novel benchmark dataset composed of 150 densely annotated video sequences. The results suggest that more research efforts should be devoted to this problem so that tracking could benefit FPV tasks. The full version of this paper is available at arXiv:2108.13665.
