CN113534945B - Method, device, equipment and storage medium for determining eye tracking calibration coefficient - Google Patents

Abstract

The embodiment of the invention provides a method, a device, equipment and a storage medium for determining an eyeball tracking calibration coefficient. Comprising the following steps: collecting eye images of a user looking at a set target point; determining calculated fixation point information corresponding to each calibration coefficient according to each stored calibration coefficient and the eye image; determining the fixation precision of each calibration coefficient according to the calculated fixation point information and the information of the set target point; and determining the calibration coefficient with the fixation precision meeting the set condition as a target calibration coefficient. According to the method for determining the eyeball tracking calibration coefficient, provided by the embodiment of the invention, the target calibration coefficient suitable for the user is determined by the user looking at the eye image of the set target point and the stored calibration coefficient, so that the calibration coefficient of the user can be rapidly determined, and the eyeball tracking efficiency is improved.

Description

Method, device, equipment and storage medium for determining eye tracking calibration coefficient

Technical Field

Embodiments of the present invention relate to the field of eye tracking technology, and in particular to a method, device, equipment and storage medium for determining an eye tracking calibration coefficient.

Background Art

When a user uses an eye tracking device for eye tracking, in order to achieve the best tracking effect, it is necessary to calibrate first and obtain a calibration coefficient for the user. This coefficient is optimal for the user's eye tracking effect in the current software environment. Therefore, it is particularly important to obtain a calibration coefficient for the user.

In the prior art, users need to calibrate the eye tracking device every time they use it, and the calibration process is complicated, which affects the efficiency of eye tracking and the user experience. Alternatively, the user identity and the calibration coefficient are bound and stored, and the identity is identified when used to obtain the user's calibration coefficient. However, this method is limited by software and hardware or user biometric information and has the disadvantage of recognition errors.

Summary of the invention

The embodiments of the present invention provide a method, device, equipment and storage medium for determining an eye tracking calibration coefficient, which can quickly determine the calibration coefficient of a user, thereby improving the efficiency of eye tracking.

In a first aspect, an embodiment of the present invention provides a method for determining an eye tracking calibration coefficient, comprising:

Collecting eye images of the user looking at a set target point;

Determine, according to the stored calibration coefficients and the eye image, the calculated gaze point information corresponding to each calibration coefficient;

Determining the gaze accuracy of each calibration coefficient according to the calculated gaze point information and the set target point information;

The calibration coefficient whose gaze accuracy satisfies the set conditions is determined as the target calibration coefficient.

In a second aspect, an embodiment of the present invention further provides a device for determining an eye tracking calibration coefficient, comprising:

An eye image acquisition module, used to acquire an eye image of a user gazing at a set target point;

A calculation gaze point information acquisition module, used to determine the calculation gaze point information corresponding to each calibration coefficient according to the stored calibration coefficients and the eye image;

A gaze accuracy determination module, used to determine the gaze accuracy of each calibration coefficient according to the calculated gaze point information and the set target point information;

The target calibration coefficient determination module is used to determine the calibration coefficient whose gaze accuracy meets the set conditions as the target calibration coefficient.

In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for determining the eye tracking calibration coefficient as described in the embodiment of the present invention is implemented.

In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processing device, implements the method for determining the eye tracking calibration coefficient as described in an embodiment of the present invention.

In this embodiment, the eye image of the user gazing at the set target point is first collected, and then the calculated gaze point information corresponding to each calibration coefficient is determined based on the stored calibration coefficients and the eye image, and then the gaze accuracy of each calibration coefficient is determined based on the calculated gaze point information and the information of the set target point, and finally the calibration coefficient whose gaze accuracy meets the set conditions is determined as the target calibration coefficient. The method for determining the eye tracking calibration coefficient provided in the embodiment of the present invention determines the target calibration coefficient suitable for the user by using the eye image of the user gazing at a set target point and the stored calibration coefficient, and can quickly determine the calibration coefficient of the user, thereby improving the efficiency of eye tracking and user experience.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for determining an eye tracking calibration coefficient in Embodiment 1 of the present invention;

FIG2 is a schematic diagram of the structure of a device for determining an eye tracking calibration coefficient in a second embodiment of the present invention;

FIG3 is a schematic diagram of the structure of a computer device in Embodiment 3 of the present invention.

DETAILED DESCRIPTION

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

Sight tracking can also be called eye tracking, which is a technology that estimates the sight direction and/or gaze point of the eyes by measuring the movement of the eyes. Specifically, the gaze point information of the user to be detected can be obtained by capturing the eye image of the user to be detected in real time, and analyzing the relative position of the eye features through the eye image of the user to be detected; or the eye movement can be detected by the capacitance value between the eyeball and the capacitor plate to obtain the gaze point information of the user to be detected; or by placing electrodes on the bridge of the nose, forehead, ears or earlobes, and detecting the eye movement through the detected myoelectric signal pattern to obtain the gaze point information of the user to be detected. Of course, other methods for obtaining the gaze point information of the user to be detected in real time can also be used, which should all fall within the protection scope of the present invention.

Tracking the eyeball can be achieved by optical recording. The principle of the optical recording method is to use an infrared camera to record the eye movement of the subject, that is, to obtain an eye image that can reflect the eye movement, and extract eye features from the obtained eye image to establish an estimation model of the line of sight. Among them, eye features may include: pupil position, pupil shape, iris position, iris shape, eyelid position, eye corner position, light spot position (or Purkinje spot), etc. The optical recording method includes the pupil-corneal reflection method. The principle of the pupil-corneal reflection method is that a near-infrared light source is directed to the eye, and the eye is photographed by an infrared camera, and the reflection point of the light source on the cornea, i.e., the light spot, is photographed at the same time, thereby obtaining an eye image with a light spot.

Of course, in addition to the optical recording method, the eye tracking device can also be a MEMS micro-electromechanical system, such as a MEMS infrared scanning reflector, an infrared light source, and an infrared receiver; or a capacitive sensor, which detects eye movement through the capacitance value between the eyeball and the capacitor plate; it can also be a myoelectric detector, which detects eye movement by placing electrodes on the bridge of the nose, forehead, ears, or earlobes, and detecting eye movement through the detected myoelectric signal pattern.

Currently, there are many ways to obtain user gaze information using eye tracking technology, and we will not list them one by one here.

Embodiment 1

FIG1 is a flow chart of a method for determining an eye tracking calibration coefficient provided in Embodiment 1 of the present invention. This embodiment is applicable to the case where a calibration coefficient is determined when a user uses an eye tracking device. The method can be executed by an eye tracking calibration coefficient determination device, which can be composed of hardware and/or software and can generally be integrated in a device having an eye tracking calibration coefficient determination function, which can be an electronic device such as a server or a server cluster. As shown in FIG1 , the method specifically includes the following steps:

Step 110: collecting an eye image of the user gazing at a set target point.

The set point may be a point displayed at a set position on the screen, or may be a position point of a designated marker in the real world, collectively referred to as a target point for the user to look at. The eye image may be a plurality of continuous eye images captured when the user looks at the set target point. In this embodiment, when the user uses the eye tracking device, the user's eyes need to be calibrated first to obtain the user's calibration coefficient, so as to track the user's line of sight according to the calibration coefficient. When the user uses the eye tracking device, the device displays or indicates a point for the user to look at, and captures a plurality of eye images of the user looking at the target point.

Step 120, determining the calculated gaze point information corresponding to each calibration coefficient according to the stored calibration coefficients and the eye image.

The stored calibration coefficients may be calibration coefficients obtained by calibrating the eyeballs of different users and having a score exceeding a set threshold, i.e., the optimal calibration coefficients. In this embodiment, one of the stored calibration coefficients may be selected as the calibration coefficient of the current user, thereby eliminating the need to recalibrate the current user. The calculated gaze point information may be the coordinate information of the gaze point calculated based on the calibration coefficients and the eye image.

Specifically, the method of determining the calculated gaze point information corresponding to each calibration coefficient based on the stored calibration coefficients and eye images can be: performing feature extraction on the eye image to obtain eye feature information; performing calculations based on the stored calibration coefficients and feature information to obtain the calculated gaze point information corresponding to each calibration coefficient.

Among them, the eye feature information may include pupil center position information and spot center position information. The eye feature information may include one or more sub-feature information, that is, feature information corresponding to one or more eye images. In this embodiment, the process of extracting features from eye images to obtain eye feature information may be: if there are multiple eye images, then extracting features from each eye image to obtain multiple sub-feature information; performing clustering algorithm selection on the multiple sub-feature information to obtain at least one optimal sub-feature information; and determining at least one optimal sub-feature information as the eye feature information.

The sub-feature information may include sub-pupil center position information and sub-spot center position information. In this embodiment, if multiple eye images of the user gazing at the set target point are collected, feature extraction is performed on each image to obtain sub-feature information corresponding to each eye image, and then a clustering algorithm is used to cluster the multiple sub-feature information to obtain one or more optimal sub-feature information, and finally the gaze point information is calculated based on the calibration coefficient and the optimal sub-feature information.

Step 130, determining the gaze accuracy of each calibration coefficient based on the calculated gaze point information and the set target point information.

The gaze accuracy may be calculated by calculating the distance between the gaze point and the set target point, or by calculating the angle between the line connecting the gaze point and the user's eyes and the line connecting the set target point and the user's eyes.

Optionally, after obtaining the calculated gaze point corresponding to each stored calibration coefficient, the distance between the calculated gaze point and the set target point is obtained, and the calculated distance is determined as the gaze accuracy.

Optionally, after obtaining the calculated gaze point corresponding to each stored calibration coefficient, the angle between the calculated gaze point and the user's eye and the line between the set target point and the user's eye is determined, and the calculated angle is determined as the gaze accuracy.

Step 140: determine the calibration coefficient whose gaze accuracy meets the set conditions as the target calibration coefficient.

Specifically, if the gaze accuracy is to calculate the distance between the gaze point and the set target point, the calibration coefficient whose gaze accuracy meets the set conditions may be determined as the target calibration coefficient by: determining the calibration coefficient whose distance is less than a first set value as the target calibration coefficient. The first set value may be a value between 0.6 mm and 2 mm.

Specifically, the gaze accuracy is the angle between the line between the gaze point and the user's eyes and the line between the set target point and the user's eyes, and the calibration coefficient whose gaze accuracy meets the set conditions is determined as the target calibration coefficient by: determining the calibration coefficient whose angle is less than the second set value as the target calibration coefficient. The second set value can be a value between 0.5 degrees and 2 degrees.

It should be noted that the specific values of the first setting value and the second setting value are determined according to the accuracy of the eye tracking device itself and the required accuracy of the actual application, and can be determined and changed according to actual needs.

In this embodiment, when determining the target calibration coefficient, it can be selected from all stored calibration coefficients, or selected from part of the calibration coefficients (such as those ranked higher in score).

The technical solution of this embodiment first collects the eye image of the user gazing at the set target point, then determines the calculated gaze point information corresponding to each calibration coefficient based on the stored calibration coefficients and the eye image, then determines the gaze accuracy of each calibration coefficient based on the calculated gaze point information and the information of the set target point, and finally determines the calibration coefficient whose gaze accuracy meets the set conditions as the target calibration coefficient. The method for determining the eye tracking calibration coefficient provided by the embodiment of the present invention determines the target calibration coefficient suitable for the user by using the eye image of the user gazing at a set target point and the stored calibration coefficient, and can quickly determine the calibration coefficient of the user, thereby improving the efficiency of eye tracking and user experience.

Optionally, before collecting the eye image of the user looking at the set target point, the following steps are also included: performing eye tracking calibration on multiple users to obtain multiple calibration coefficients; calculating the score of each calibration coefficient, and determining the calibration coefficient whose score exceeds a set threshold as the optimal calibration coefficient; and storing the optimal calibration coefficient.

Among them, the process of performing eye tracking calibration on the user and obtaining the calibration coefficient can be: collecting eye images of the user gazing at multiple target points; using a clustering algorithm to process multiple eye images corresponding to each target point to obtain at least one optimal eye image corresponding to each target point; and calculating the calibration coefficient based on at least one eye image corresponding to each target point.

Specifically, the method of processing the multiple eye images corresponding to each target point using a clustering algorithm may be, for the multiple eye images corresponding to the current target point, first extracting feature information of each eye image to obtain multiple sub-feature information, and then clustering the multiple sub-feature information using a clustering algorithm to obtain one or more optimal sub-feature information of the current target point. Correspondingly, the method of calculating the calibration coefficient according to at least one eye image corresponding to each target point may be to calculate the calibration coefficient according to one or more optimal sub-feature information corresponding to each target point.

The method of calculating the score of the calibration coefficient may be: determining the gaze accuracy of the calculated gaze point according to the calibration coefficient and at least one optimal eye image corresponding to each target point; and calculating the score of the corresponding calibration coefficient according to the gaze accuracy of the calculated gaze point. The lower the value of the gaze accuracy, the higher the score of the corresponding calibration coefficient.

The gaze accuracy may be the distance between the calculated gaze point and the set target point, or the angle between the calculated gaze point and the user's eyes and the set target point and the user's eyes. Specifically, for the current target point, the calculated gaze point information corresponding to the current target point is obtained according to one or more optimal sub-characteristic information and the calibration coefficient of the current target point, and then the gaze accuracy of the current calculated gaze point is calculated according to the calculated gaze point information and the current target point information.

In this embodiment, the way to calculate the score of the calibration coefficient according to the gaze accuracy of each calculated gaze point can be to obtain the average gaze accuracy of each calculated gaze point, and determine the score of the calibration coefficient according to the average gaze accuracy. In this embodiment, the corresponding relationship between the gaze accuracy and the score can be preset, and a mapping table of the gaze accuracy and the calibration coefficient score can be created according to the corresponding relationship. Specifically, after obtaining the calibration coefficient, the gaze accuracy of each calculated gaze point is determined according to the calibration coefficient, and the average gaze accuracy is obtained, and then the score corresponding to the average gaze accuracy is found from the mapping table, so as to obtain the score of the calibration coefficient. Exemplarily, assuming that the gaze accuracy of each calculated gaze point is 0.3, 0.33, 0.44, 0.35, and 0.46, and the average gaze accuracy is 0.376, the score corresponding to 0.376 is found from the mapping table to be 92 points, that is, the score of the calibration coefficient is 92 points. If the threshold is set to 90 points, the calibration coefficient with a score of 92 is determined as the optimal calibration coefficient and stored.

In this embodiment, the calibration coefficients whose scores exceed the set threshold are stored for subsequent use by users who use the eye tracking device. There is no need to recalibrate the users who use the eye tracking device, which can improve the efficiency of eye tracking.

Embodiment 2

Fig. 2 is a schematic diagram of the structure of an eye tracking calibration coefficient determination device provided by Embodiment 2 of the present invention. As shown in Fig. 2, the device comprises: an eye image acquisition module 210, a gaze point information acquisition module 220, a gaze accuracy determination module 230 and a target calibration coefficient determination module 240.

Eye image acquisition module 210, used to acquire an eye image of a user gazing at a set target point;

A calculated fixation point information acquisition module 220, configured to determine the calculated fixation point information corresponding to each calibration coefficient according to each stored calibration coefficient and the eye image;

A gaze accuracy determination module 230, configured to determine the gaze accuracy of each calibration coefficient according to the calculated gaze point information and the set target point information;

The target calibration coefficient determination module 240 is used to determine the calibration coefficient whose gaze accuracy meets the set conditions as the target calibration coefficient.

Optionally, the module for obtaining the gaze point information calculation 220 is further configured to:

Extracting features from the eye image to obtain eye feature information; the eye feature information includes pupil center position information and light spot center position information;

Calculation is performed based on the stored calibration coefficients and the eye feature information to obtain calculated gaze point information corresponding to each calibration coefficient.

Optionally, the module for obtaining the gaze point information calculation 220 is further configured to:

If there are multiple eye images, feature extraction is performed on each eye image to obtain multiple sub-feature information;

A clustering algorithm is performed on the multiple sub-feature information to obtain at least one optimal sub-feature information; and the at least one optimal sub-feature information is determined as eye feature information.

Optionally, the gaze accuracy determination module 230 is further configured to:

Obtaining the distance between the calculated gaze point and the set target point, and determining the distance as the gaze accuracy;

Optionally, the target calibration coefficient determination module 240 is further configured to:

The calibration coefficient whose distance is smaller than the first set value is determined as the target calibration coefficient.

Optionally, the gaze accuracy determination module 230 is further configured to:

Determine and calculate the angle between the line between the gaze point and the user's eyes and the line between the set target point and the user's eyes, and determine the angle as the gaze accuracy;

Optionally, the target calibration coefficient determination module 240 is further configured to:

The calibration coefficient whose included angle is smaller than the second set value is determined as the target calibration coefficient.

Optionally, it also includes: an optimal calibration coefficient acquisition module, used to:

Perform eye tracking calibration on multiple users to obtain multiple calibration coefficients;

Calculate the score of each calibration coefficient, and determine the optimal calibration coefficient for the calibration coefficient whose score exceeds the set threshold;

The optimal calibration coefficient is stored.

Optionally, the optimal calibration coefficient acquisition module is also used to:

Collect eye images of the user gazing at multiple target points;

A clustering algorithm is used to process multiple eye images corresponding to each target point to obtain at least one optimal eye image corresponding to each target point;

The calibration coefficient is calculated according to at least one eye image corresponding to each target point.

Optionally, the optimal calibration coefficient acquisition module is also used to:

Calculating the gaze accuracy of each target point according to the calibration coefficient and at least one optimal eye image corresponding to each target point;

The score of the calibration coefficient is calculated according to the gaze accuracy of each target point.

The above device can execute the methods provided by all the above embodiments of the present invention, and has the corresponding functional modules and beneficial effects of executing the above methods. For technical details not described in detail in this embodiment, please refer to the methods provided by all the above embodiments of the present invention.

Embodiment 3

FIG3 is a block diagram of a computer device provided in Embodiment 3 of the present invention. FIG3 shows a block diagram of a computer device 312 suitable for implementing the present invention. The computer device 312 shown in FIG3 is only an example and should not limit the functions and scope of use of the present invention. Device 312 is a typical computing device for determining the eye tracking calibration coefficient.

As shown in Fig. 3, computer device 312 is in the form of a general-purpose computing device. Components of computer device 312 may include, but are not limited to: one or more processors 316, storage device 328, and bus 318 connecting different system components (including storage device 328 and processor 316).

Bus 318 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

The computer device 312 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 312, including volatile and non-volatile media, removable and non-removable media.

The storage device 328 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 330 and/or cache memory 332. The computer device 312 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, the storage system 334 may be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 3 , commonly referred to as a “hard drive”). Although not shown in FIG. 3 , a disk drive for reading and writing removable non-volatile disks (such as “floppy disks”) and an optical disk drive for reading and writing removable non-volatile optical disks (such as compact disc-read only memory (CD-ROM), digital video discs (DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 318 via one or more data media interfaces. The storage device 328 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present invention.

A program 336 having a set (at least one) of program modules 326 may be stored, for example, in a storage device 328, such program modules 326 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 326 generally perform the functions and/or methods of the embodiments described herein.

The computer device 312 may also communicate with one or more external devices 314 (e.g., keyboard, pointing device, camera, display 324, etc.), may also communicate with one or more devices that enable a user to interact with the computer device 312, and/or communicate with any device that enables the computer device 312 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input/output (I/O) interface 322. In addition, the computer device 312 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN) and/or a public network, such as the Internet) through a network adapter 320. As shown, the network adapter 320 communicates with other modules of the computer device 312 through a bus 318. It should be understood that although not shown in the figure, other hardware and/or software modules can be used in conjunction with the computer device 312, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) systems, tape drives, and data backup storage systems.

The processor 316 executes various functional applications and data processing by running the programs stored in the storage device 328, such as implementing the method for determining the eye tracking calibration coefficient provided in the above embodiment of the present invention.

Embodiment 4

The embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processing device, a method for determining an eye tracking calibration coefficient in an embodiment of the present invention is implemented. The computer-readable medium of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: receives a source text input by a user, translates the source text into a target text corresponding to a target language; obtains the user's historical correction behavior; corrects the target text according to the historical correction behavior, obtains a translation result, and pushes the translation result to the client where the user is located.

Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and/or flow chart, and the combination of the square boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A method for determining an eye tracking calibration coefficient, comprising: Collecting eye images of the user looking at a set target point; Determine, according to the stored calibration coefficients and the eye image, the calculated gaze point information corresponding to each calibration coefficient; Determining the gaze accuracy of each calibration coefficient according to the calculated gaze point information and the set target point information; Determine a calibration coefficient whose gaze accuracy satisfies a set condition as a target calibration coefficient, wherein the target calibration coefficient is selected from the stored calibration coefficients; The stored calibration coefficients are obtained by: performing eye tracking calibration on multiple users to obtain multiple calibration coefficients; calculating the score of each calibration coefficient, and determining the calibration coefficient whose score exceeds a set threshold as the optimal calibration coefficient; and storing the optimal calibration coefficient. 2. The method according to claim 1, characterized in that determining the calculated gaze point information corresponding to each calibration coefficient respectively according to the stored calibration coefficients and the eye image comprises: Extracting features from the eye image to obtain eye feature information; the eye feature information includes pupil center position information and light spot center position information; Calculation is performed based on the stored calibration coefficients and the eye feature information to obtain calculated gaze point information corresponding to each calibration coefficient. 3. The method according to claim 2, characterized in that extracting features from the eye image to obtain eye feature information comprises: If there are multiple eye images, feature extraction is performed on each eye image to obtain multiple sub-feature information; A clustering algorithm is performed on the multiple sub-feature information to obtain at least one optimal sub-feature information; and the at least one optimal sub-feature information is determined as eye feature information. 4. The method according to claim 1, characterized in that determining the gaze accuracy of each calibration coefficient according to the calculated gaze point information and the set target point information comprises: Obtaining the distance between the calculated gaze point and the set target point, and determining the distance as the gaze accuracy; Accordingly, the calibration coefficient whose gaze accuracy meets the set conditions is determined as the target calibration coefficient, including: The calibration coefficient for which the distance is smaller than the first set value is determined as the target calibration coefficient. 5. The method according to claim 1, characterized in that determining the gaze accuracy of each calibration coefficient according to the calculated gaze point information and the set target point information comprises: Determine and calculate the angle between the line between the gaze point and the user's eyes and the line between the set target point and the user's eyes, and determine the angle as the gaze accuracy; Accordingly, the calibration coefficient whose gaze accuracy meets the set conditions is determined as the target calibration coefficient, including: The calibration coefficient whose angle is smaller than the second set value is determined as the target calibration coefficient. 6. The method according to claim 1, characterized in that performing eye tracking calibration on the user to obtain the calibration coefficient comprises: Collect eye images of the user gazing at multiple target points; A clustering algorithm is used to process multiple eye images corresponding to each target point to obtain at least one optimal eye image corresponding to each target point; The calibration coefficient is calculated according to at least one optimal eye image corresponding to each target point. 7. The method according to claim 6, wherein calculating the score of each calibration coefficient comprises: Calculating the gaze accuracy of each target point according to the calibration coefficient and at least one optimal eye image corresponding to each target point; The score of the calibration coefficient is calculated according to the gaze accuracy of each target point. 8. A device for determining an eye tracking calibration coefficient, comprising: An eye image acquisition module, used to acquire an eye image of a user gazing at a set target point; A calculation gaze point information acquisition module, used to determine the calculation gaze point information corresponding to each calibration coefficient according to the stored calibration coefficients and the eye image; A gaze accuracy determination module, used to determine the gaze accuracy of each calibration coefficient according to the calculated gaze point information and the set target point information; A target calibration coefficient determination module, used to determine a calibration coefficient whose gaze accuracy meets a set condition as a target calibration coefficient, wherein the target calibration coefficient is selected from the stored calibration coefficients; The stored calibration coefficients are obtained by: performing eye tracking calibration on multiple users to obtain multiple calibration coefficients; calculating the score of each calibration coefficient, and determining the calibration coefficient whose score exceeds a set threshold as the optimal calibration coefficient; and storing the optimal calibration coefficient. 9. A computer device, characterized in that the device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for determining the eye tracking calibration coefficient as described in any one of claims 1 to 7 is implemented. 10. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processing device, the method for determining an eye tracking calibration coefficient as described in any one of claims 1 to 7 is implemented.