Predicting user visual attention in virtual reality with a deep learning model

Recent studies show that user's visual attention during virtual reality museum navigation can be effectively estimated with deep learning models. However, these models rely on large-scale datasets that usually are of high structure complexity and context specific, which is challenging for nonspecialist researchers and designers. Therefore, we present the deep learning model, ALRF, to generalise on real-time user visual attention prediction in virtual reality context. The model combines two parallel deep learning streams to process the compact dataset of temporal-spatial salient features of user's eye movements and virtual object coordinates. The prediction accuracy outperformed the state-of-the-art deep learning models by reaching record high 91.03%. Importantly, with quick parametric tuning, the model showed flexible applicability across different environments of the virtual reality museum and outdoor scenes. Implications for how the proposed model may be implemented as a generalising tool for adaptive virtual reality application design and evaluation are discussed.

Automated summary

Recent studies have shown that user's visual attention during virtual reality museum navigation can be estimated effectively with deep learning models. The ALRF model, which combines temporal-spatial features of user's eye movements and virtual object coordinates, outperformed state-of-the-art models with a prediction accuracy of 91.03%, demonstrating flexibility across different virtual reality environments.