Tobii Ab

Yimu Wang

Pravin Kumar Rana

Mark Ryan

Torbjörn Sundberg

Gilfredo Remon Salazar

Tiesheng Wang

Simon Johansson

Mikael Rosell

Johannes Kron

Rickard Lundahl

Joakim Zachrisson

Ylva Björk

Macarena Garcia Romero

Mark Ainsley Ryan

An eye tracking system configured to: receive a plurality of right eye images of a right eye of a user; receive a plurality of left eye images of a left eye of a user, each left eye image corresponding to a right eye image in the plurality of right eye images; detect a pupil and determine an associated pupil signal, for each of the plurality of right eye images and each of the plurality of left eye images; calculate a right eye pupil variation of the pupil signals for the plurality of right eye images and a left eye pupil variation of the pupil signals for the plurality of left eye images; and determine a right eye weighting and a left eye weighting based on the right eye pupil variation and the left eye pupil variation. For one or more right eye images and one or more corresponding left eye images, the eye tracking system can: determine at least one right eye gaze signal based on the right eye image and at least one left eye gaze signal based on the corresponding left eye image; and calculate a combined gaze signal from a weighted sum of the right eye gaze signal and the left eye gaze signal using the right eye weighting and the left eye weighting.

Eye gaze tracking system, associated methods and computer programs

Images of an eye are captured by a camera. For each of the images, gaze data is obtained and a position of a pupil center is estimated in the image. The gaze data indicates a gaze point and/or gaze direction of the eye when the image was captured. A mapping is calibrated using the obtained gaze data and the estimated positions of the pupil center. The mapping maps positions of the pupil center in images captured by the camera to gaze points at a surface, or to gaze directions. A further image of the eye is captured by the camera. A position of the pupil center is estimated in the further image. Gaze tracking is performed using the calibrated mapping and the estimated position of the pupil center in the further image. These steps may for example be performed at a HMD.

Gaze tracking using mapping of pupil center position

An eye tracking system configured to: receive a plurality of right-eye-images of a right eye of a user; receive a plurality of left-eye-images of a left eye of a user, each left-eye-image corresponding to a right-eye-image in the plurality of right-eye-images; detect a pupil and determine an associated pupil-signal, for each of the plurality of right-eye-images and each of the plurality of left-eye-images; calculate a right-eye-pupil-variation of the pupil-signals for the plurality of right-eye-images and a left-eye-pupil-variation of the pupil-signals for the plurality of left-eye-images; and determine a right-eye-weighting and a left-eye-weighting based on the right-eye-pupil-variation and the left-eye-pupil-variation. For one or more right-eye-images and one or more corresponding left-eye-images, the eye tracking system can: determine at least one right-eye-gaze-signal based on the right-eye-image and at least one left-eye-gaze-signal based on the corresponding left-eye-image; and calculate a combined-gaze-signal from a weighted sum of the right-eye-gaze-signal and the left-eye-gaze-signal using the right-eye-weighting and the left-eye-weighting.

Disclosed is a method for detecting a shadow in an image of an eye region of a user wearing a Head Mounted Device, HMD. The method comprises obtaining, from a camera of the HMD, an image of the eye region of the user wearing a HMD and determining an area of interest in the image, the area of interest comprising a plurality of subareas. The method further comprises determining a first brightness level for a first subarea of the plurality of subareas and determining a second brightness level for a second subarea of the plurality of subareas. The method further comprises comparing the first brightness level with the second brightness level, and, based on the comparing, selectively generating a signal indicating a shadow.

Method and device for detecting a shadow in a head mounted device

Images of an eye are captured at respective time instances by a camera of a head-mounted device. For each time instance, a position of a center of corneal curvature is estimated using an image captured at that time instance, a position of a pupil center is estimated using an image captured at that time instance, and a line is determined through the estimated corneal curvature center position and the estimated pupil center position. A first estimated position of a center of the eye is computed based on the lines determined for time instances in a first time period. A second estimated position of the center of the eye is computed based on the lines determined for time instances in a second time period. Relocation of the head-mounted device relative to a user's head is detected based on the first and second estimated positions of the eye center.

Detecting relocation of a head-mounted device

A method of identifying scleral reflections in an eye tracking system is disclosed. A glint is identified in an image from an image sensor, wherein the glint is a representation in the image of a reflection of light from a cornea of the eye of the user or from a sclera of the eye of the user. A first pixel intensity of the glint is determined, a second pixel intensity of neighbor pixels of the glint is determined, and an absolute value of the difference between the first pixel intensity of the glint and the second pixel intensity of the neighbor pixels of the glint is determined. The glint is identified as a representation of a reflection from the sclera of the eye of the user on condition that the determined absolute value of the difference is below a predetermine threshold value.

Method and system for glint/reflection identification

The invention is related to a method for calibrating an eye tracking device within a head-mounted display (HMD) comprising the steps of acquiring with the HMD via an image sensor, at least one optical target image from an optical target, wherein the optical target contains image points in a pattern, indexing image points within the optical target image wherein the image points are indexed by, selecting a rigid region of the optical target image, assigning indices to image points within the rigid region, fitting a polynomial approximation function to at least one column and one row of the image points of the region, predicting the location of at least one image point using the fitted polynomial approximation function, assigning the predicted image point an index, inputting indexed image points into an optimization algorithm that calculates a hardware calibration of the HMD, and writing hardware calibration values calculated from the optimization algorithm to the HMD unit.

Iterative point indexing

A method for determining eye openness with an eye tracking device is disclosed. The method may include determining, for pixels of an image sensor of an eye tracking device, during a first time period when an eye of a user is open, a first sum of intensity of the pixels. The method may also include determining, during a second time period when the eye of the user is closed, a second sum of intensity of the pixels. The method may further include determining, during a third time period, a third sum of intensity of the pixels. The method may additionally include determining that upon the third sum exceeding a fourth sum of the first sum plus a threshold amount, that the eye of the user is closed, the threshold amount is equal to a product of a threshold fraction and a difference between the first sum and the second sum.

Determining eye openness with an eye tracking device

An image of an eye of a user from an image sensor can be received, the image resulting from the image sensor detecting light from one or more illuminators reflected from the eye of the user. A glint is identified in the image as a representation in the image of a reflection of light from a cornea of the eye of the user or from a sclera of the eye of the user. A first pixel intensity of the glint is determined, a second pixel intensity of neighbor pixels of the glint is determined, and an absolute value of the difference between the first pixel intensity and the second pixel intensity is determined. The glint is identified as a representation of a reflection from the sclera of the eye of the user on condition that the determined absolute value of the difference is below a predetermine threshold value.

An image of an eye of a user can be received from an image sensor, the image resulting from the image sensor detecting light from one or more illuminators reflected from the eye of the user and reflected from optic arrangements located between the one or more illuminators and the eye of the user, wherein the optic arrangements comprise features having a known pattern. Glints are identified in the image, wherein a glint is a representation in the image of a reflection of light from a cornea of the eye of the user and/or from a feature of the optic arrangements. Relative positions in the image of the identified glints are determined, and on condition that the relative positions conform with the known pattern, at least one glint of the identified glints is identified as a representation in the image of a reflection from a feature of the optic arrangements.

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Danderyd, Sweden

Yimu Wang