360VOT: A New Benchmark Dataset for Omnidirectional Visual Object
  Tracking
Huajian Huang Yinzhe Xu Yingshu Chen Sai-Kit Yeung
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