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: https://360vot.hkustvgd.com

翻译：360{\deg}图像能够提供全方位的视野，这对于稳定和长期的场景感知至关重要。本文探索了360{\deg}图像在视觉目标跟踪中的应用，并剖析了由大畸变、拼接伪影及其他360{\deg}图像特有属性所引发的新挑战。为解决这些问题，我们利用目标定位的新颖表示形式（即边界视场）提出了一种通用360跟踪框架，能够适配典型跟踪器以实现全方位跟踪。更关键的是，我们提出了大规模全方位跟踪基准数据集360VOT，以推动未来研究。该数据集包含120个序列，采用等距柱状投影格式生成高达113K张高分辨率帧，跟踪目标覆盖32个类别，场景多样。此外，我们提供了4类无偏真值（包括旋转边界框和旋转边界视场），以及针对360{\deg}图像定制的评估指标，能够精确评估全方位跟踪性能。最后，我们系统评估了20种最先进的视觉跟踪器，并为未来对比研究提供了新基线。主页：https://360vot.hkustvgd.com