  Panoramic video provides immersive and interactive experience by enabling
humans to control the field of view (FoV) through head movement (HM). Thus, HM
plays a key role in modeling human attention on panoramic video. This paper
establishes a database collecting subjects' HM in panoramic video sequences.
From this database, we find that the HM data are highly consistent across
subjects. Furthermore, we find that deep reinforcement learning (DRL) can be
applied to predict HM positions, via maximizing the reward of imitating human
HM scanpaths through the agent's actions. Based on our findings, we propose a
DRL-based HM prediction (DHP) approach with offline and online versions, called
offline-DHP and online-DHP. In offline-DHP, multiple DRL workflows are run to
determine potential HM positions at each panoramic frame. Then, a heat map of
the potential HM positions, named the HM map, is generated as the output of
offline-DHP. In online-DHP, the next HM position of one subject is estimated
given the currently observed HM position, which is achieved by developing a DRL
algorithm upon the learned offline-DHP model. Finally, the experiments validate
that our approach is effective in both offline and online prediction of HM
positions for panoramic video, and that the learned offline-DHP model can
improve the performance of online-DHP.
