Tobii Ab

Tobil Ab

Oscar Danielsson

Tommaso Martini

Anders Dahl

Patrik Barkman

Mårten Nilsson

Carl Asplund

Maria Gordon

Alexander Davies

Daniel Johansson Torneus

David Molin

A method for training an eye tracking model is disclosed, as well as a corresponding system and storage medium. The eye tracking model is adapted to predict eye tracking data based on sensor data from a first eye tracking sensor. The method comprises receiving sensor data obtained by the first eye tracking sensor at a time instance and receiving reference eye tracking data for the time instance generated by an eye tracking system comprising a second eye tracking sensor. The reference eye tracking data is generated by the eye tracking system based on sensor data obtained by the second eye tracking sensor at the time instance. The method comprises training the eye tracking model based on the sensor data obtained by the first eye tracking sensor at the time instance and the generated reference eye tracking data.

Training an eye tracking model

A system and a method for determining a gaze direction of a user viewing a scene is provided. The system comprises a camera for obtaining an image of at least one of the user's eyes, a depth information detection device for obtaining depth data related to the image, and a processing unit. The processing unit is configured to define a surface at a predetermined position relative to the user's eye based on the depth data, obtain a normalized image by projecting the image onto the surface and determine a normalized gaze direction based on the normalized image. The processing unit is further configured to determine a gaze direction based on the normalized gaze direction and the depth data.

Determining a gaze direction using depth information

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

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Danderyd, Sweden

Oscar Danielsson