Tobii Ab

Tobil Ab

Alexander Davies

Maria Gordon

Oscar Danielsson

Tommaso Martini

Per-Edvin Stoltz

David Molin

There is disclosed a computer implemented eye tracking system and corresponding method and computer readable storage medium, for detecting three dimensional, 3D, gaze, by obtaining at least one head pose parameter using a head pose prediction algorithm, the head pose parameter(s) comprising one or more of a head position, pitch, yaw, or roll; and to input the at least one head pose parameter along with at least one image of a user's eye, generated from a 2D image captured using an image sensor associated with the eye tracking system, into a neural network configured to generate 3D gaze information based on the at least one head pose parameter and the at least one eye image.

Deep learning for three dimensional (3D) gaze prediction

A system and techniques for calibrating an eye tracking system are described. The system can update the calibration of personal calibration parameters continuously based on a user's gaze on a user interface, following user interface stimulus events. The system improves continuous calibration techniques by determining an association between the user's eye sequences and the stimulus events, and updates the personal calibration parameters accordingly. A record indicative of a user gaze, including eye sequences, such as eye movements or eye fixations, is maintained over a time period. A user interface stimulus event associated with the user interface and occurring within the time period is detected. An association is determined between the eye sequence and the user interface stimulus event. An interaction observation that includes the eye sequence and a location of the stimulus event is generated. Personal calibration parameters are updated based on the interaction observation.

Method to reliably detect correlations between gaze and stimuli

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Danderyd, Sweden

Alexander Davies