Peripheral vision enables humans to see shapes that aren't directly in our line of sight, albeit with less detail. This ability expands our field of vision and can be helpful in many situations, such as detecting a vehicle approaching our car from the side.
Unlike humans, AI does not have peripheral vision. Equipping computer vision models with this ability could help them detect approaching hazards more effectively or predict whether a human driver would notice an oncoming object.
Taking a step in this direction, MIT researchers developed an image dataset that allows them to simulate peripheral vision in machine learning models. They found that training models with this dataset improved the models' ability to detect objects in the visual periphery, although the models still performed worse than humans.
Their results also revealed that, unlike with humans, neither the size of objects nor the amount of visual clutter in a scene had a strong impact on the AI's performance.
"There is something fundamental going on here. We tested so many different models, and even when we train them, they get a little bit better but they are not quite like humans. So, the question is: What is missing in these models?" says Vasha DuTell, a postdoc and co-author of a paper detailing this study.
Answering that question may help researchers build machine learning models that can see the world more like humans do. In addition to improving driver safety, such models could be used to develop displays that are easier for people to view.
Plus, a deeper understanding of peripheral vision in AI models could help researchers better predict human behavior, adds lead author Anne Harrington MEng '23.
