Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system configured to enable operation of an apparatus based on the gaze of a user, the system comprising a processor, and a memory comprising instructions executable by the processor, wherein the system is configured to determine a gaze region of a user among a plurality of regions associated with the apparatus, wherein the plurality of regions comprises at least one primary gaze region and at least one secondary gaze region and perform at least one action based on the determination of the gaze region, wherein the system is configured to determine the gaze region using a first gaze estimation algorithm and/or a second gaze estimation algorithm.

Abstract: 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.

Abstract: The disclosure relates to a method performed by a computer for identifying a space that a user of a gaze tracking system is viewing, the method comprising obtaining gaze tracking sensor data, generating gaze data comprising a probability distribution using the sensor data by processing the sensor data by a trained model and identifying a space that the user is viewing using the probability distribution.

Abstract: 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.