An active foveated gaze prediction algorithm based on a Bayesian ideal observer

Predicting human eye movements is a crucial task for understanding human behavior and has numerous applications in machine vision. Most current models for predicting eye movements are data-driven and require large datasets of recorded eye movements, which can be expensive and time-consuming to col-lect. In this paper, we present a novel theory-based model for predicting eye movements in a foveated visual system that maximizes information gain at each fixation. Our model uses a region-proposal net-work and eccentricity-based max pooling to account for the loss of detail in peripheral vision. We apply our model to predict human fixations in a visual search task for objects in real-world scenes. Unlike data-driven models, our model does not require training on large eye movement datasets and can generalize to any set of natural images and targets. We evaluate the generalization capability of our model by demon-strating its results on two publicly available visual search datasets, Ehinger and COCO-search18, without any further training on those datasets. Our model outperforms or performs comparably to data-driven models that are directly trained on human eye movement datasets. & COPY; 2023 Published by Elsevier Ltd.

Automated summary

This paper presents a theory-based model for predicting eye movements in a foveated visual system, which can generalize to any set of natural images and targets without the need for large eye movement datasets. The model outperforms or performs comparably to data-driven models trained on human eye movement datasets.