Systems and methods of capturing eye-gaze data

A system for collecting eye-gaze data as training data for an eye-gaze prediction model, the system comprising:

• a processor;
• and
• a memory storing computer-executable instructions that when executed by the processor cause the system to:
• select a scan path from a set of predetermined scan paths, wherein each scan path is non-self-overlapping on a screen of a device, the scan path passes through a point in a region of a series of regions in a grid on the screen, and the point represents an expected value of uniformly distributed random eye-gaze pints in the region;
• display a symbol as an eye-gaze target on the screen, wherein the eye-gaze target moves along the scan path for guiding attention of the operator;
• receive a combination of eye-gaze data and input images associated with a plurality of points along the scan path as training data for the eye-gaze prediction model;
• train the eye-gaze prediction model using the training data, wherein the eye-gaze prediction model includes data associated with parameters in one or more neural networks;
• and
• update the parameters in the one or more neural networks using the trained eye-gaze prediction model.

A computer-readable recording medium storing computer-executable instructions that when executed by a processor cause a computer system to:

• select a scan path from a set of predetermined scan paths, wherein each scan path is non-self-overlapping on a screen of a device, the scan path passes through a point in a region of a series of regions in a grid on the screen, and the point represents an expected value of uniformly distributed random eye-gaze points in the region;
• display a symbol as an eye-gaze target on the screen, wherein the eye-gaze target moves along the scan path for guiding attention of the operator;
• receive a combination of eye-gaze data and input images associated with a plurality of points along the scan path as training data for the eye-gaze prediction model;
• train the eye-gaze prediction model using the training data, wherein the eye-gaze prediction model includes data associated with parameters in one or more neural networks;
• and
• update the parameters in the one or more neural networks using the trained eye-gaze prediction model.