Regional eye movement tracking user parameter calibration method

Abstract

The embodiment of the invention provides a regional eye movement tracking user parameter calibration method. The method comprises the following steps: collecting a calibration eye pattern of each calibration point watched by a human eye; determining a calibrated pupil feature and a calibrated light spot feature of each calibration point; calculating a calibrated cornea curvature radius and a calibrated cornea curvature center of each calibration point according to the calibrated pupil feature and the calibrated light spot feature of each calibration point; determining the calibration optical axis direction of each calibration point and the distance from the calibration pupil center to the calibration cornea curvature center according to the calibration cornea curvature center and the calibration pupil center of each calibration point; calculating an included angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point; and collecting an actual eye pattern, and calculating the actual optical axis direction of the actual eye pattern according to the included angle and the actual optical axis direction of the actual eye pattern. By performing regional calibration on each calibration point, the eye movement tracking accuracy and precision are improved.

Description

TECHNICAL FIELD

[0001] The embodiments of the present application belong to the technical field of eye tracking, and particularly relate to a regional eye movement tracking user parameter calibration method. BACKGROUND

[0002] As one of the important organs of the body, the eyes provide 80% of the information obtained by the brain in daily life, and thus the research on gaze estimation has become a hot field. A common method for gaze estimation is to place two or more light sources in front of the eyes. When the light emitted from the light sources passes through the eyes, reflection and refraction are formed on the boundary of the corneal surface. In eye movement tracking technology, since there are large differences in the eyeball parameters of each individual, the user's eyeball parameters such as the corneal curvature radius (R) and the angle between the optical axis and the visual axis (Kappa angle) need to be calibrated before eye movement tracking. The current main calibration method is to use a fixed number of calibration points (generally 5-11) to calibrate each eyeball parameter. Only one set of calibration parameters is obtained for each eye. However, in eye movement tracking, the mathematical model of the eye is often assumed to be an ideal double-sphere model or a regular geometric model. However, the actual eyeball is irregular, and the parameters of the eyeball relative to the visual point are different when looking in different directions. Therefore, the accuracy of eye movement tracking is low, and the user experience is poor. SUMMARY

[0003] To solve or alleviate the technical problems in the prior art, the regional eyeball parameter calibration method is proposed in the embodiments of the present application. Different eyeball parameters are taken when the eyes look in different directions to estimate the gaze, improve the accuracy of eye movement tracking, and improve the user experience.

[0004] The embodiments of the present application provide a regional eye movement tracking user parameter calibration method, which comprises:

[0005] Collecting a calibration eye map of each calibration point of human eye fixation;

[0006] Determining calibration pupil features and calibration spot features of each calibration point, wherein the pupil features include a calibration pupil center;

[0007] Calculating a calibration corneal curvature radius and a calibration corneal curvature center of each calibration point according to the calibration pupil features and the calibration spot features of each calibration point;

[0008] Determining a calibration optical axis direction and a distance from the calibration pupil center to the calibration corneal curvature center of each calibration point according to the calibration corneal curvature center and the calibration pupil center of each calibration point;

[0009] Calculating an angle between the calibration optical axis direction and a calibration visual axis direction of each calibration point;

[0010] collecting an actual eye diagram, calculating an actual optical axis direction of the actual eye diagram according to actual pupil characteristics and actual light spot characteristics of the actual eye diagram and the calibration pupil characteristics of each calibration point, the calibration corneal curvature radius of the calibration eye and the distance from the calibration pupil center to the calibration corneal curvature center of the calibration eye;

[0011] calculating a visual axis direction of the actual eye diagram according to the optical axis direction of the actual eye diagram and the angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point.

[0012] As a preferred embodiment of the present application, the calibration pupil characteristics and the calibration light spot characteristics of each calibration point are determined by:

[0013] inputting each frame of the calibration eye diagram into a pupil target detection network for pupil position detection, and cutting out a pupil region picture; inputting the pupil region picture into a pupil feature detection model to detect calibration pupil characteristics;

[0014] inputting the pupil region picture into a light spot detection model to detect calibration light spot characteristics;

[0015] calculating the calibration pupil characteristics and the calibration light spot characteristics of each calibration point according to the calibration pupil characteristics and the calibration light spot characteristics of each frame of the calibration eye diagram.

[0016] As a preferred embodiment of the present application, the calibration pupil characteristics and the calibration light spot characteristics of each calibration point are calculated according to the calibration pupil characteristics and the calibration light spot characteristics of each frame of the calibration eye diagram, comprising:

[0017] dividing the calibration pupil characteristic positions of each frame of the calibration eye diagram obtained by each calibration point into N groups of data according to x, y and z coordinate axes;

[0018] obtaining the calibration pupil characteristics and the calibration light spot characteristics of each calibration point by clustering the N groups of data of each calibration point respectively through a clustering algorithm.

[0019] As a preferred embodiment of the present application, the calibration corneal curvature radius and the calibration corneal curvature center of each calibration point are calculated according to the calibration pupil characteristics and the calibration light spot characteristics of each calibration point, comprising:

[0020] calculating the calibration corneal curvature radius and the calibration corneal curvature center of each frame of the calibration eye diagram according to the calibration pupil characteristics and the calibration light spot characteristics of each frame of the calibration eye diagram;

[0021] dividing the calibration corneal curvature radius and the calibration corneal curvature center of each frame of the calibration eye diagram obtained by each calibration point into M groups of data according to x, y and z coordinate axes;

[0022] The M groups of data of each of the calibration points are clustered by a clustering algorithm to obtain a calibration corneal curvature radius and a calibration corneal curvature center of each of the calibration points.

[0023] As a preferred embodiment of the present application, the determination of the calibration optical axis direction of each of the calibration points according to the calibration corneal curvature center and the calibration pupil center of each of the calibration points comprises:

[0024] According to the calibration corneal curvature center and the calibration pupil center of each of the calibration points, the calibration optical axis direction and the distance from the calibration corneal curvature center to the calibration pupil center of each of the calibration points are determined.

[0025] The calibration optical axis direction and the distance from the calibration corneal curvature center to the calibration pupil center of each of the calibration points are clustered by a clustering algorithm to obtain the distance from the calibration corneal curvature center to the calibration pupil center of each of the calibration points and the calibration optical axis direction of each of the calibration points.

[0026] As a preferred embodiment of the present application, the calculation of the actual optical axis direction of the actual eye map comprises:

[0027] An actual eye map is collected, and an actual pupil feature and an actual light spot feature are obtained by a pupil feature detection model and a light spot detection model, wherein the actual pupil feature comprises an actual pupil center.

[0028] A weight is calculated according to the reciprocal of the distance between the actual pupil center of the actual eye map and the calibration pupil center of each of the calibration points, and the actual corneal curvature radius is obtained by summing the product of the weight and the calibration corneal curvature radius of each of the calibration points.

[0029] The actual pupil center to actual corneal curvature center distance of the actual eye map is obtained by summing the product of the weight and the distance from the calibration corneal curvature center to the calibration pupil center of each of the calibration points.

[0030] The actual optical axis direction of the actual eye map is obtained according to the direction of the line between the actual corneal curvature center and the actual pupil center.

[0031] As a preferred embodiment of the present application, the calculation of the actual visual axis direction of the actual eye map according to the actual optical axis direction of the actual eye map and the angle between the calibration optical axis direction and the calibration visual axis direction of each of the calibration points comprises:

[0032] The weight is calculated according to the reciprocal of the distance between the actual optical axis direction of the actual eye diagram and the calibration optical axis direction of each calibration point, and the perpendicular component of the actual optical axis direction and the actual visual axis direction of the actual eye diagram is obtained by summing the product of the weight and the perpendicular component of the angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point.

[0033] The horizontal component of the actual optical axis direction and the actual visual axis direction of the actual eye diagram is obtained by summing the product of the weight and the horizontal component of the angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point.

[0034] The actual visual axis direction of the actual eye diagram is calculated according to the horizontal component and the perpendicular component of the actual optical axis direction and the actual visual axis direction of the actual eye diagram.

[0035] Compared with the prior art, the embodiment of the present application provides a sub-regional eye movement tracking user parameter calibration method, which refines the eye movement parameters of different visions by sub-regional calibration of each calibration point, improves the accuracy and precision of eye movement tracking by selectively using the eye parameters in actual use, enhances the customer experience, and meets more application scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0036] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of this application and illustrate exemplary embodiments of the present application and are used to explain the present application, but do not constitute improper limitations on the present application. Some specific embodiments of the present application will be described in detail hereinafter with reference to the accompanying drawings in an exemplary and non-limiting manner. The same reference signs in the drawings indicate the same or similar components or parts, and it should be understood by those skilled in the art that the drawings are not necessarily drawn to scale, and in the drawings:

[0037] Figure 1 is a sub-regional eye movement tracking user parameter calibration method flowchart provided by the embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the present application scheme, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0039] As shown in Figure 1 The embodiment of the present application provides a sub-regional eye movement tracking user parameter calibration method, and the method comprises the following steps:

[0040] Step S101, collect the calibration eye map of human eye gaze at each calibration point;

[0041] First, the calibration program needs to be designed to determine the number and location of calibration points, the position of the infrared lamp relative to the camera optical center, and the number of frames required for each calibration point during calibration:

[0042] Design the calibration program, determine 9 calibration points (according to demand, generally speaking, the more calibration points, the higher the accuracy, but the longer the calibration time required, generally 5-11 calibration points) and specific coordinates of calibration in the calibration program. The position of the calibration point refers to the 3D coordinates of the calibration point .

[0043] The coordinate position of the infrared lamp relative to the camera optical center can be determined from the structure size of the head-mounted device The present application provides an embodiment of 8 infrared lamps (note: the infrared light line enters the cornea and forms 8 light spots on the cornea).

[0044] The calibration program guides the human eye to gaze at each calibration point, and each calibration point collects images Collect multiple images at each calibration point, generally speaking, the more frames, the smaller the statistical error, and the present application embodiment collects 20 images at each calibration point for parameter calibration.

[0045] In the image , c represents the calibration point number, , and f represents the sequence number of the collected image frames at each calibration point .

[0046] Step S102, determine the calibration pupil feature and calibration light spot feature of each calibration point;

[0047] Specifically, it includes:

[0048] The collected calibration eye map is input into the pupil target detection network to detect the pupil position and crop the pupil area according to the pupil position (i.e. ), which is mainly to reduce interference and improve the inference speed of the later model.

[0049] The pupil target detection model mentioned above has a typical architecture: U-Net, DeepLabv3+, which can be a model based on semantic segmentation. The calibration eye map is regarded as a pixel-level binary classification task (pupil vs. non-pupil), and a segmentation mask with the same size as the input is output by the model, where each pixel value represents the probability of belonging to the pupil. The model mainly performs morphological operations and ellipse fitting on the segmented pupil area to obtain the center coordinates and contour from the fitted ellipse.

[0050] The pupil area obtained above Input into the pupil feature detection model and the light spot detection ranking model to obtain the calibrated pupil key points of each frame of calibrated eye map containing the pupil center and the 8 key point positions of the pupil edge) and the calibrated light spot feature, that is

[0051] The pupil feature detection model mentioned above refers to a model that uses a deep learning model to automatically extract and quantify a series of high-dimensional feature vectors that describe the shape, dynamics, and texture characteristics of the pupil from the calibrated eye map. Typical architecture: HRNet or EfficientNet.

[0052] The light spot detection ranking model mentioned above specifically refers to a deep learning model that automatically detects, identifies, and ranks the importance of multiple corneal reflection points (light spots) in ophthalmic imaging (such as corneal topography, surgical microscope). The core goal is to accurately lock the key reference point for calculating the Kappa angle from multiple interfering reflections that may exist.

[0053] The calibrated pupil center obtained from the pupil key points mentioned above There is a calibrated pupil center for each frame of calibrated eye map, and the calibrated pupil center of each calibration point is obtained through the kmeans clustering algorithm (Supplementary explanation: for example, the calibrated pupil center of the 20 frames of calibrated eye map at calibration point 0 is Divide the calibrated pupil center into 3 groups of data according to the x, y, z coordinate axes , and , respectively, and perform clustering on these three groups of data through the kmeans algorithm, with the cluster number k being 3. The cluster center with the largest number of clusters is the final value, such as Through three clustering, the cluster center value with the largest number of clusters is , so the calibrated pupil center at calibration point 0 is , and the calibrated pupil center at other calibration points can be obtained in the same way.)

[0054] Step S103, calculating the calibrated corneal curvature radius and the calibrated corneal curvature center of each calibration point according to the calibrated pupil feature and the calibrated light spot feature of each calibration point.

[0055] Specifically, it includes:

[0056] First, according to the Le Grand full theoretical eye model and the reflection light principle through the light spot feature, the calibrated corneal curvature center and the curvature radius ​​(Note: calibration eye corneal curvature center and curvature radius The specific process of calculation is not expanded in detail, because it is not the focus of the present application).

[0057] The principle of reflected light is mainly that the infrared light emitted by the light source has a light spot on the human eye, and the light spot is imaged on the camera after being reflected by the human eye. At the same time, the pupil center of the human eye is also imaged on the camera. Through the imaging of the pupil center on the camera and the imaging of the light spot on the camera, the calibration eye corneal curvature center of each frame of calibration eye image can be obtained by the iterative minimum value method and the calibration eye corneal curvature radius .

[0058] The Le Grand mathematical model is a complete optical system parameter set composed of multiple optical surfaces (cornea, lens) and their media, and the core purpose is to theoretically calculate (such as retinal imaging size, aberration analysis), rather than to accurately simulate a specific human eye.

[0059] Subsequently, the calibration eye corneal curvature center and the calibration eye corneal curvature radius at each calibration point are calculated, and the above steps obtain one calibration eye corneal curvature center and one calibration eye corneal curvature radius for each frame of calibration eye image. If there are 9 calibration points, then 9 (number of calibration points) * 20 (number of image frames collected at each calibration point) = 180 calibration eye corneal curvature centers and calibration eye corneal curvature radii. This step determines the calibration eye corneal curvature center (only one coordinate) and the curvature radius (only one value) of each calibration point. Now take calibration point 0 as an example for calculation:

[0060] Obtain the calibration eye corneal curvature center at calibration point 0: through the above steps, the calibration eye corneal curvature center of the cornea at calibration point 0 is obtained for 20 frames of calibration eye images. At the same time, the calibration eye corneal curvature center at this time is divided into three groups of data , and according to the x, y, and z coordinate axes. Through the kmeans algorithm, the three groups of data are clustered respectively, the cluster number k is 3, and the cluster center of the most clustered cluster is the final value. Thus, the calibration eye corneal curvature center at calibration point 0 is .

[0061] Obtain the curvature radius at calibration point 0: through the above steps, the calibration eye corneal curvature radius is obtained . Through the kmeans algorithm, the three groups of data are clustered respectively, the cluster number k is 3, and the cluster center of the most clustered cluster is the final value. Thus, the curvature radius at calibration point 0 is .

[0062] Similarly, repeat the above steps for each calibration point to obtain the calibration corneal curvature center of each calibration point as and the calibration corneal curvature radius as (Note: The calibration corneal curvature center is used for calculating other parameters of the eyeball).

[0063] Step S104, according to the calibration corneal curvature center and the calibration pupil center of each of the calibration points, determine the calibration optical axis direction and the distance from the calibration pupil center to the calibration corneal curvature center of each of the calibration points.

[0064] Through the above steps, the calibration corneal curvature center is obtained as and the calibration corneal curvature radius is Take the calibration pupil key point and the calibration light spot feature as the reference, and according to the principle of reflected light, the distance from the calibration corneal curvature center to the calibration pupil center of each frame of calibration eye image and the calibration optical axis direction of each frame of calibration eye image are obtained.

[0065] Use the kmeans clustering algorithm to cluster the distance from the calibration corneal curvature center to the calibration pupil center of each frame of calibration eye image and the calibration optical axis direction of each frame of calibration eye image, to obtain the distance from the calibration corneal curvature center to the calibration pupil center of each calibration point , and the calibration optical axis direction of each calibration point is .

[0066] Step S105, calculate the included angle between the calibration optical axis direction and the calibration visual axis direction of each of the calibration points.

[0067] The optical axis is the direction of the line connecting the calibration corneal curvature center and the calibration pupil center, and the visual axis is the line connecting the calibration corneal curvature center and the fovea on the retina (i.e. the continuous direction of the calibration point ), according to the projection relationship between the optical axis and the visual axis, the horizontal and vertical components of the Kappa angle and .

[0068] Thus, each calibration point has a set of calibration parameters, such as: the calibration parameters at calibration point 0 are: .

[0069] Step S106, collect the actual eye image and calculate the actual optical axis direction of the actual eye image.

[0070] It should be noted that when estimating the actual visual line, the above calibration parameters are used for visual line estimation.

[0071] First, the actual eye image is collected in real time, and the actual pupil feature is obtained through the pupil feature detection and light spot detection algorithm (Wherein the actual pupil center ) and the actual spot feature , wherein the actual spot feature is the spot position and spot serial number on the actual eye chart.

[0072] According to the actual pupil center of the actual eye chart of this frame The reciprocal of the distance between the actual pupil center and the calibration pupil center pC1(x, y, z) of the calibration point is used to calculate the weight ww i The actual corneal curvature radius of the actual eye chart of this frame is obtained by weighting And the distance between the actual pupil center and the actual corneal curvature center .

[0073]

[0074]

[0075] Wherein, norm is the function of calculating the distance between two points.

[0076] Step S107, according to the actual optical axis direction of the actual eye chart and the angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point, the visual axis direction of the actual eye chart is calculated.

[0077] Through the actual pupil feature The actual spot feature And the actual corneal curvature radius rr and the distance between the actual pupil center and the actual corneal curvature center dd obtained in the last step, the actual optical axis direction of the actual eye chart of this frame can be obtained according to the 3D visual line algorithm In the embodiments of the present application, the 3D visual line algorithm is prior art, which will not be described here.

[0078] According to the distance between the actual optical axis direction of the actual eye chart at this time and the calibration optical axis direction of the calibration point, the weight wp i The horizontal component Kappa1 and the vertical component Kappa2 of the Kappa angle of the current frame are obtained by weighting:

[0079]

[0080] Wherein, n = 8 is a c++ library function.

[0081] According to the Kappa1 and Kappa2 obtained in the last step, the final eye movement direction or gaze point direction is obtained, and the eye movement tracking is completed.

[0082] The embodiment of the application refines different visual eye movement parameters through regional calibration, improves eye movement tracking accuracy and precision by selectively using eye parameters in actual use, enhances customer experience, reduces eye parameter errors of different vision, improves the accuracy and precision of line of sight estimation, and is used in eye tracking of VR / AR headsets.

[0083] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for calibrating user parameters in regional eye-tracking, characterized in that, include: Collect calibration eye diagrams of the human eye fixating on each calibration point; Determine the calibration pupil features and calibration spot features for each calibration point, wherein the pupil features include the calibration pupil center; Calculate the calibrated corneal curvature radius and calibrated corneal curvature center for each calibration point based on the calibrated pupil characteristics and calibrated light spot characteristics for each calibration point; The calibration optical axis direction and the distance from the calibration pupil center to the calibration corneal curvature center are determined based on the calibration corneal curvature center and calibration pupil center of each calibration point; Calculate the angle between the calibration optical axis direction and the calibration visual axis direction at each calibration point; Acquire actual eye images, and calculate the actual optical axis direction of the actual eye images based on the actual pupil characteristics, actual light spot characteristics, and calibrated pupil characteristics at each calibration point, the calibrated corneal curvature radius, and the distance from the calibrated pupil center to the calibrated corneal curvature center. The visual axis direction of the actual eye diagram is calculated based on the optical axis direction of the actual eye diagram and the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point.

2. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The determination of the calibration pupil features and calibration spot features for each calibration point includes: Each frame of the calibrated eye map is input into the pupil target detection network to detect the pupil position, and the pupil region image is cropped out; the pupil region image is input into the pupil feature detection model to detect the calibrated pupil features; The pupil region image is input into the spot detection model to detect and calibrate spot features; The calibration pupil features and calibration spot features of each calibration point are calculated based on the calibration pupil features and calibration spot features of the calibration eye diagram in each frame.

3. The method for calibrating user parameters for regional eye tracking as described in claim 2, characterized in that, The step of calculating the calibration pupil features and calibration spot features for each calibration point based on the calibration pupil features and calibration spot features of each frame of the calibration eye diagram includes: The calibrated pupil feature positions of each frame of the calibrated eye map obtained at each calibration point are divided into N groups of data according to the x, y, z coordinate axes; After clustering the N sets of data for each calibration point using a clustering algorithm, the calibration pupil features and calibration spot features for each calibration point are obtained.

4. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The step of calculating the calibrated corneal curvature radius and calibrated corneal curvature center for each calibrated point based on the calibrated pupil characteristics and calibrated light spot characteristics of each calibrated point includes: The calibrated corneal curvature radius and calibrated corneal curvature center of each frame of the calibrated eye diagram are calculated based on the calibrated pupil features and calibrated spot features of each frame of the calibrated eye diagram. The calibrated corneal curvature radius and calibrated corneal curvature center of each frame of the calibrated eye map obtained at each calibrated point are divided into M groups of data according to the x, y, z coordinate axes; After clustering the M sets of data for each calibration point using a clustering algorithm, the calibrated corneal curvature radius and the calibrated corneal curvature center of each calibration point are obtained.

5. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The step of determining the calibration optical axis direction of each calibration point based on the calibration corneal curvature center and calibration pupil center of each calibration point includes: Based on the calibrated corneal curvature center and calibrated pupil center of each frame of the calibrated eye diagram, determine the calibrated optical axis direction and the distance from the calibrated corneal curvature center to the calibrated pupil center of each frame of the calibrated eye diagram; The calibration optical axis direction and the distance from the calibration corneal curvature center to the calibration pupil center of each frame of the calibration eye map are clustered using a clustering algorithm to obtain the distance from the calibration corneal curvature center to the pupil center and the calibration optical axis direction of each calibration point.

6. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The calculation of the actual optical axis direction of the actual eye diagram includes: A real eye image is acquired, and the actual pupil features and actual light spot features are obtained through a pupil feature detection model and a light spot detection model. The actual pupil features include the actual pupil center. The weight is calculated by multiplying the weight by the distance between the actual pupil center of the actual eye diagram and the calibrated pupil center of each calibration point, and then summing the products of the weights and the calibrated corneal curvature radius of each calibration point to obtain the actual corneal curvature radius. The actual distance from the center of the pupil to the center of the actual corneal curvature of the actual eye diagram is obtained by summing the products of the weights and the distance from the center of the calibrated corneal curvature to the center of the calibrated pupil at each calibration point. The actual optical axis direction of the actual eye diagram is obtained by the direction of the line connecting the actual corneal curvature center and the actual pupil center.

7. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The step of calculating the actual visual axis direction of the actual eye diagram based on the actual optical axis direction of the actual eye diagram and the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point includes: The weight is calculated based on the reciprocal of the distance between the actual optical axis direction of the actual eye diagram and the calibration optical axis direction of each calibration point. The vertical component of the actual optical axis direction and the actual visual axis direction of the actual eye diagram is obtained by summing the product of the weight and the vertical component of the angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point. The horizontal components of the actual optical axis direction and the actual visual axis direction of the actual eye diagram are obtained by summing the product of the weight and the horizontal component of the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point; The actual visual axis direction of the actual eye diagram is calculated based on the horizontal and vertical components of the actual optical axis direction and the actual visual axis direction of the actual eye diagram.

Images