Abstract: A system, a head-mounted device, a computer program, a carrier, and a method for a head-mounted device comprising an eye tracking sensor, for updating an eye tracking model in relation to an eye are disclosed. First sensor data in relation to the eye are obtained by means of the eye tracking sensor. After obtaining the first sensor data, the eye tracking sensor is moved in relation to the eye. After moving the eye tracking sensor, second sensor data in relation to the eye are obtained by means of the eye tracking sensor. The eye tracking model in relation to the eye is then updated based on the first sensor data and the second sensor data.

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: A system, a head-mounted device, a computer program, a carrier, and a method for a head-mounted device comprising an eye tracking sensor, for updating an eye tracking model in relation to an eye are disclosed. First sensor data in relation to the eye are obtained by means of the eye tracking sensor. After obtaining the first sensor data, the eye tracking sensor is moved in relation to the eye. After moving the eye tracking sensor, second sensor data in relation to the eye are obtained by means of the eye tracking sensor. The eye tracking model in relation to the eye is then updated based on the first sensor data and the second sensor data.