Virtual reality and machine learning for predicting visual attention in a daylit exhibition space: A proof of concept

Abstract

Lighting features act as driving forces that control visual attention and perception. VR can analyse the relationship between human behavior and environment stimuli, while machine learning (ML) can exploit these data to develop a predictive model. This paper aims to develop an approach for predicting visual attention using VR and ML algorithms. VR was applied to experiment 4 virtual 360 scenes of a generic daylit exhibition space, and 41 participants were recruited. Measurements of head tracking were investigated to capture the areas of interest (AoI) within the space under different daylight conditions. Features were extracted to train the ML models through luminance, spatial contrast, and center bias. Results showed the potential of ML for predicting the visual behaviour. When comparing predicted with measured data, the accuracy reached 71 % using ensemble bagged trees algorithm. This study acts as a proof-of-concept for predicting visual attention using ML algorithms, highlighting the potentials of ML and VR for adopting human-centric design. Thus, it allows architects and lighting designers to promote more interesting and interacting visual experience.