360{\deg} images can provide an omnidirectional field of view which is
important for stable and long-term scene perception. In this paper, we explore
360{\deg} images for visual object tracking and perceive new challenges caused
by large distortion, stitching artifacts, and other unique attributes of
360{\deg} images. To alleviate these problems, we take advantage of novel
representations of target localization, i.e., bounding field-of-view, and then
introduce a general 360 tracking framework that can adopt typical trackers for
omnidirectional tracking. More importantly, we propose a new large-scale
omnidirectional tracking benchmark dataset, 360VOT, in order to facilitate
future research. 360VOT contains 120 sequences with up to 113K high-resolution
frames in equirectangular projection. The tracking targets cover 32 categories
in diverse scenarios. Moreover, we provide 4 types of unbiased ground truth,
including (rotated) bounding boxes and (rotated) bounding field-of-views, as
well as new metrics tailored for 360{\deg} images which allow for the accurate
evaluation of omnidirectional tracking performance. Finally, we extensively
evaluated 20 state-of-the-art visual trackers and provided a new baseline for
future comparisons. Homepage: this https URL

本文研究探索了如何利用 360° 图像进行视觉目标的追踪，并介绍了由于大变形、拼接伪影和其他独特属性所带来的新挑战。为了解决这些问题，文中提出了一种新颖的目标定位表示方式 —— 边界视野，并引入了一个通用的 360° 追踪框架，该框架可以用于全向追踪。此外，为了促进未来的研究，作者提出了一个新的大规模全向追踪基准数据集 ——360VOT，其中包含 120 个不同场景中的序列，共有 113K 个高分辨率的等距投影帧。追踪目标涵盖 32 个类别。另外，作者还提供了 4 种无偏的基准数据，包括（旋转的）边界框和（旋转的）边界视野，并提出了适用于 360° 图像的新度量标准，以便对全向追踪性能进行准确评估。最后，作者还对 20 种最先进的视觉追踪算法进行了广泛评估，并为未来的比较提供了新的基准。