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 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 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: A method for determining gaze calibration parameters for gaze estimation of a viewer using an eye-tracking system. The method comprises obtaining a set of data points including gaze tracking data of the viewer and position information of at least one target visual; selecting a first subset of the data points and determining gaze calibration parameters using said first subset. A score for the gaze calibration parameters is determined by using the gaze calibration parameters with a second subset of data points, wherein at least one data point of the subset is not included in the first subset. The score is indicative of the capability of the gaze calibration parameters to reflect position information of the at least one target visual based on the gaze tracking data. The score is compared to a candidate score and if it is higher, the calibration parameters are set to the candidate calibration parameters and the score to the candidate score.

Abstract: A method for determining gaze calibration parameters for gaze estimation of a viewer using an eye-tracking system. The method comprises obtaining a set of data points including gaze tracking data of the viewer and position information of at least one target visual; selecting a first subset of the data points and determining gaze calibration parameters using said first subset. A score for the gaze calibration parameters is determined by using the gaze calibration parameters with a second subset of data points, wherein at least one data point of the subset is not included in the first subset. The score is indicative of the capability of the gaze calibration parameters to reflect position information of the at least one target visual based on the gaze tracking data. The score is compared to a candidate score and if it is higher, the calibration parameters are set to the candidate calibration parameters and the score to the candidate score.

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.