Guides
What makes a good eye tracker?
There is no single specification that makes an eye tracker good.
Accuracy matters, but so do robustness, calibration, the data available to you, how easily the system fits into your experiment or workflow, and what you can do with the data afterwards. A system that performs well in one controlled test may still introduce limitations when used by different people, in different environments, or alongside other hardware and software.
A good eye tracker should place as few constraints as possible between you and the question you want to answer. That applies whether eye tracking is used in a research study, an industrial workflow, or a product or application.
At Pupil Labs, we have spent more than a decade working on these problems. Our focus is on making eye tracking work without calibration, across diverse people and environments, while providing access to rich data, adaptable hardware, open interfaces, and tools for analysis.
In this series, we look at the different parts of an eye tracking system that determine how well it works in practice, why they matter, and what to consider when choosing an eye tracker.