Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach

TL;DR

This paper introduces a deep reinforcement learning method to predict head movements in panoramic video, leveraging a new database and demonstrating effectiveness in both offline and online scenarios.

Contribution

It presents a novel DRL-based approach for head movement prediction in panoramic video, including offline and online models, supported by a new HM database and validation experiments.

Findings

High consistency of HM data across subjects

DRL effectively predicts HM positions

Offline model enhances online prediction performance

Abstract

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.