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 rack for a cooking appliance includes a perimeter rim that has a first end, a second end, a first side rail, and a second side rail. A plurality of spaced rails are disposed between each of the first and second rails and the first and second ends of the perimeter rim. An elongated member is operably coupled to the perimeter rim and is proximate to the plurality of spaced rails. A crossbar is disposed proximate to the elongated member. The crossbar defines an opening directly behind the elongated member along a length of the elongated member and a length of the crossbar.

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: 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: A rack for a cooking appliance includes a perimeter rim that has a first end, a second end, a first side rail, and a second side rail. A plurality of spaced rails are disposed between each of the first and second rails and the first and second ends of the perimeter rim. An elongated member is operably coupled to the perimeter rim and is proximate to the plurality of spaced rails. A crossbar is disposed proximate to the elongated member. The crossbar defines an opening directly behind the elongated member along a length of the elongated member and a length of the crossbar.

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 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: 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.