  Salient human detection (SHD) in dynamic 360{\deg} immersive videos is of
great importance for various applications such as robotics, inter-human and
human-object interaction in augmented reality. However, 360{\deg} video SHD has
been seldom discussed in the computer vision community due to a lack of
datasets with large-scale omnidirectional videos and rich annotations. To this
end, we propose SHD360, the first 360{\deg} video SHD dataset which contains
various real-life daily scenes. Since so far there is no method proposed for
360{\deg} image/video SHD, we systematically benchmark 11 representative
state-of-the-art salient object detection (SOD) approaches on our SHD360, and
explore key issues derived from extensive experimenting results. We hope our
proposed dataset and benchmark could serve as a good starting point for
advancing human-centric researches towards 360{\deg} panoramic data. The
dataset is available at https://github.com/PanoAsh/SHD360.
