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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 containing various real-life daily scenes borrowed from http://hidden.for.anonymity, with hierarchical annotations for 6,268 key frames uniformly sampled from 37,403 omnidirectional video frames at 4K resolution. 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 approaches on our SHD360. We hope our proposed dataset and benchmark could serve as a good starting point for advancing human-centric researches towards 360{\deg} panoramic data. Our dataset and benchmark will be publicly available at https://github.com/PanoAsh/SHD360.
