Abstract: The disclosure relates to a method of estimating a three-dimensional position of at least one target object performed by a computer, the method comprising obtaining a first image, detecting at least one target object, generating a set of images depicting subsets of a captured scene, and estimating a three-dimensional position of at least one target object using a probability distribution (P) indicative of a likelihood that the at least one target object is in a particular position (x, y, z) in the scene.

Abstract: The disclosure relates to a method of estimating a three-dimensional position of at least one target object performed by a computer, the method comprising obtaining a first image, detecting at least one target object, generating a set of images depicting subsets of a captured scene, and estimating a three-dimensional position of at least one target object using a probability distribution (P) indicative of a likelihood that the at least one target object is in a particular position (x, y, z) in the scene.

Abstract: The invention is related to a method of estimating an orientation of an object in an image, comprising the steps of: calculating, for the object in the image, a probability distribution of rotation; and estimating the orientation of the object from the calculated probability distribution; wherein the step of calculating the probability distribution and/or the step of estimating the orientation of the object are executed by a neural network; wherein the probability distribution is a matrix Fisher probability density function; and wherein the step of calculating the probability distribution includes approximating a normalizing function for the matrix Fisher probability density function.

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 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: 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 gaze tracking model is adapted to predict a gaze ray using an image of the eye. The model is trained using training data which comprises a first image of an eye, reference gaze data indicating a gaze point towards which the eye was gazing when the first image was captured, and images of an eye captured by first and second cameras at a point in time. The training comprises forming a distance between the gaze point and a gaze ray predicted by the model using the first image, forming a consistency measure based on a gaze ray predicted by the model using the image captured by the first camera and a gaze ray predicted by the model using the image captured by the second camera, forming an objective function based on at least the formed distance and the consistency measure, and training the model using the objective function.