Eye movement data determination method and electronic equipment

Abstract

The invention provides an eye movement data determination method and electronic equipment, which are used for improving the detection efficiency of eye movement data. Comprising the steps that after an eye movement function is started, a pre-stored configuration file is obtained, the configuration file comprises a device type and an eye movement tracking algorithm identifier, and the device type is used for identifying the type corresponding to eye movement hardware equipment in the device; based on the equipment type, determining a target image acquisition algorithm corresponding to the equipment type from preset image acquisition algorithms; based on the eye movement tracking algorithm identifier, determining a target eye movement tracking algorithm corresponding to the eye movement tracking algorithm identifier from preset eye movement tracking algorithms; acquiring image data by using the target image acquisition algorithm every specified duration; and sending the image data to the target eye movement tracking algorithm to obtain eye movement data corresponding to the image data.

Description

Background Art

[0002] With the rapid development of virtual reality and augmented reality technologies, eye tracking technology has become one of the key technologies. Through eye tracking technology, virtual scenes can be more intelligent and user experience can be more interactive. Therefore, eye tracking technology has great development potential in the fields of virtual reality and augmented reality. The development of eye tracking technology has promoted the diversification and advancement of related hardware equipment. Its corresponding eye tracking hardware equipment includes eye tracking sensors, eye tracking glasses, and iris tracking devices, etc., which continuously meet the needs of different fields and different scenarios. At the same time, different eye tracking algorithms will appear in the software layer of eye tracking technology.

[0003] However, the implementation of eye tracking solutions is extremely dependent on related eye movement hardware devices, and the diversity of hardware eye movement devices leads to very different corresponding eye tracking algorithms. In virtual reality devices and augmented reality devices, corresponding eye tracking algorithms are also required. Therefore, the integration of eye tracking algorithms in device systems will have different methods due to the differences in eye movement hardware devices. In this way, the different eye tracking algorithms used by different manufacturers of virtual reality devices and augmented reality devices will increase the difficulty of device system and application development adaptation, which is not conducive to the development of the entire software ecosystem. And if different types of eye movement hardware devices are replaced in virtual reality devices and augmented reality devices, a corresponding eye tracking algorithm needs to be reconfigured in the software layer of the device, resulting in low efficiency in eye movement data detection. Summary of the invention

[0004] The present application provides a method for determining eye movement data and an electronic device for improving the detection efficiency of eye movement data.

[0005] In a first aspect, an embodiment of the present application provides a method for determining eye movement data, the method comprising:

[0006] When the eye tracking function is started, a pre-stored configuration file is obtained, wherein the configuration file includes a device type and an eye tracking algorithm identifier, wherein the device type is used to identify the type corresponding to the eye tracking hardware device in the device;

[0007] Based on the device type, determining a target image acquisition algorithm corresponding to the device type from various preset image acquisition algorithms; and,

[0008] Based on the eye tracking algorithm identifier, determining a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from preset eye tracking algorithms;

[0009] At specified time intervals, the target image acquisition algorithm is used to acquire image data; and,

[0010] The image data is sent to the target eye tracking algorithm to obtain eye movement data corresponding to the image data.

[0011] A second aspect of the present application provides an electronic device, including a processor and a memory, wherein the processor and the memory are connected via a bus;

[0012] The memory stores a computer program, and the processor is configured to perform the following operations based on the computer program:

[0013] When the eye tracking function is started, a pre-stored configuration file is obtained, wherein the configuration file includes a device type and an eye tracking algorithm identifier, wherein the device type is used to identify the type corresponding to the eye tracking hardware device in the device;

[0014] Based on the device type, determining a target image acquisition algorithm corresponding to the device type from various preset image acquisition algorithms; and,

[0015] Based on the eye tracking algorithm identifier, determining a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from preset eye tracking algorithms;

[0016] At specified time intervals, the target image acquisition algorithm is used to acquire image data; and,

[0017] The image data is sent to the target eye tracking algorithm to obtain eye movement data corresponding to the image data.

[0018] According to a third aspect provided by an embodiment of the present invention, a computer storage medium is provided, wherein the computer storage medium stores a computer program, and the computer program is used to execute the method as described in the first aspect.

[0019] In the above embodiment of the present application, when the eye movement function is started, a pre-stored configuration file is obtained, and based on the device type in the configuration file, a target image acquisition algorithm corresponding to the device type is determined from the preset image acquisition algorithms; and based on the eye tracking algorithm identifier in the configuration file, a target eye tracking algorithm corresponding to the preset eye tracking algorithm identifier is determined from the preset eye tracking algorithms; image data is acquired using the target image acquisition algorithm at specified intervals; and the image data is sent to the target eye tracking algorithm to obtain eye movement data corresponding to the image data. Therefore, in the embodiment of the present application, image data acquisition algorithms corresponding to eye movement hardware devices of multiple device types and eye tracking algorithms corresponding to eye movement hardware devices of multiple types are pre-set, which reduces the adaptation difficulty of system and application development, and if different types of eye movement hardware devices are replaced in virtual reality devices and augmented reality devices, there is no need to reconfigure a set of corresponding eye tracking algorithms in the software layer of the device, and only needs to switch to the eye tracking algorithm corresponding to the eye movement hardware device, thereby improving the detection efficiency of eye movement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 The following is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0022] Figure 2 A schematic diagram showing an exemplary flow chart of a method for determining eye movement data provided in an embodiment of the present application;

[0023] Figure 3 A schematic diagram showing the correspondence between device type and image acquisition algorithm provided in an embodiment of the present application is exemplified;

[0024] Figure 4 A schematic diagram showing the correspondence between an eye tracking algorithm identifier and an eye tracking algorithm provided in an embodiment of the present application is exemplarily shown;

[0025] Figure 5 The following is a schematic diagram of a process flow corresponding to when eye movement data is not obtained according to an embodiment of the present application;

[0026] Figure 6 The interface diagram provided by the embodiment of the present application is exemplarily shown;

[0027] Figure 7 A system architecture diagram showing an exemplary method for determining eye movement data provided by an embodiment of the present application;

[0028] Figure 8 The flowchart of the method for determining eye movement data provided by the embodiment of the present application is exemplarily shown;

[0029] Fig. 9 A schematic diagram exemplarily shows a device for determining eye movement data provided in an embodiment of the present application;

[0030] Fig.10 The hardware structure diagram of the electronic device provided in the embodiment of the present application is exemplified. DETAILED DESCRIPTION

[0031] In order to make the purpose, implementation mode and advantages of the present application clearer, the exemplary implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the exemplary embodiments of the present application. Obviously, the described exemplary embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0032] Based on the exemplary embodiments described in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the claims attached to this application. In addition, although the disclosure in this application is introduced according to one or several exemplary examples, it should be understood that each aspect of the disclosure can also constitute a complete implementation method separately.

[0033] It should be noted that the brief description of terms in this application is only for the convenience of understanding the embodiments described below, and is not intended to limit the embodiments of this application. Unless otherwise specified, these terms should be understood according to their common and usual meanings.

[0034] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover but not exclude inclusion, for example, a product or device comprising a series of components is not necessarily limited to those components clearly listed, but may include other components not clearly listed or inherent to these products or devices.

[0035] The term "module" as used in this application refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0036] The idea of ​​the embodiments of the present application is summarized below.

[0037] At present, the implementation of eye tracking solutions is extremely dependent on related eye movement hardware devices, and the diversity of hardware eye movement devices leads to very different corresponding eye tracking algorithms. In virtual reality devices and augmented reality devices, corresponding eye tracking algorithms are also required. Therefore, the integration of eye tracking algorithms in device systems will have different methods due to the differences in eye movement hardware devices. The different eye tracking algorithms used by different manufacturers of virtual reality devices and augmented reality devices will increase the difficulty of device system and application development adaptation, which is not conducive to the development of the entire software ecosystem. And if different types of eye movement hardware devices are replaced in virtual reality devices and augmented reality devices, a corresponding eye tracking algorithm needs to be reconfigured in the software layer of the device, resulting in low efficiency in eye movement data detection.

[0038] Based on the existing technology, due to the different eye tracking algorithms used by different manufacturers of virtual reality devices and augmented reality devices, the difficulty of device system and application development adaptation will be increased, and if different types of eye movement hardware devices are replaced in virtual reality devices and augmented reality devices, it is necessary to reconfigure a set of corresponding eye tracking algorithms in the software layer of the device, which leads to the problem of low detection efficiency of eye movement data. The embodiment of the present application provides a method for determining eye movement data, which is to obtain a pre-stored configuration file after the eye movement function is started, and determine the target image acquisition algorithm corresponding to the device type from the preset image acquisition algorithms based on the device type in the configuration file; and determine the target eye tracking algorithm corresponding to the eye tracking algorithm identifier from the preset eye tracking algorithms based on the eye tracking algorithm identifier in the configuration file; obtain image data using the target image acquisition algorithm at specified intervals; and send the image data to the target eye tracking algorithm to obtain eye movement data corresponding to the image data. Therefore, in the embodiment of the present application, image data acquisition algorithms corresponding to multiple types of eye movement hardware devices and eye tracking algorithms corresponding to multiple types of eye movement hardware devices are pre-set, which reduces the adaptation difficulty of system and application development. Moreover, if different types of eye movement hardware devices are replaced in virtual reality devices and augmented reality devices, there is no need to reconfigure a corresponding eye tracking algorithm in the software layer of the device. Instead, it is only necessary to switch to the eye tracking algorithm corresponding to the eye movement hardware device, thereby improving the detection efficiency of eye movement data.

[0039] like Figure 1As shown, an application scenario of a method for determining eye movement data, in which the application scenario is explained by taking the electronic device as a VR device as an example, and the application scenario includes a VR device 110 and a memory 120.

[0040] In a possible application scenario, when the eye movement function of the VR device 110 is started, a pre-stored configuration file is obtained from the memory 120, wherein the configuration file includes a device type and an eye tracking algorithm identifier, wherein the device type is used to identify the type corresponding to the eye movement hardware in the device; then the VR device 110 determines a target image acquisition algorithm corresponding to the device type from various preset image acquisition algorithms based on the device type; and determines a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from various preset eye tracking algorithms based on the eye tracking algorithm identifier; at specified time intervals, the VR device 110 acquires image data using the target image acquisition algorithm; and sends the image data to the target eye tracking algorithm to obtain eye movement data corresponding to the image data.

[0041] in, Figure 1 The VR device 110 and the storage 120 can exchange information through a communication network, wherein the communication method adopted by the communication network can be divided into a wireless communication method or a wired communication method.

[0042] Exemplarily, the VR device 110 can access the network through cellular mobile communication technology to communicate with the storage 120, wherein the cellular mobile communication technology, for example, includes the fifth generation mobile communication (5th Generation Mobile Networks, 5G) technology.

[0043] Optionally, the VR device 110 can access the network via short-range wireless communication to communicate with the storage 120, wherein the short-range wireless communication method, for example, includes Wireless Fidelity (Wi-Fi) technology.

[0044] Among them, the description in this application only details a single VR device 110 and a single memory 120, but those skilled in the art should understand that the VR device 110 and the single memory 120 shown are intended to represent the operation of the VR device 110 and the single memory 120 involved in the technical solution of this application. It does not imply any limitation on the number, type or location of the VR device 110 and the single memory 120. It should be noted that if additional modules are added to the illustrated environment or individual modules are removed from it, the underlying concept of the exemplary embodiment of this application will not be changed.

[0045] It should be noted that the method for determining eye movement data proposed in this application is not only applicable to Figure 1 The application scenario shown is also applicable to any determination device with eye movement data.

[0046] The following describes the method for determining eye movement data in an exemplary embodiment of the present application in combination with the application scenarios described above and with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the methods and principles of the present application, and the implementation methods of the present application are not limited in this respect.

[0047] The following is a detailed description of the method for determining eye movement data in the embodiment of the present application in conjunction with the accompanying drawings. Figure 2 FIG. 1 is a flow chart of a method for determining eye movement data, which may include the following steps:

[0048] Step 201: when the eye movement function is activated, a pre-stored configuration file is obtained, wherein the configuration file includes a device type and an eye movement tracking algorithm identifier, wherein the device type is used to identify the type corresponding to the eye movement hardware device in the device;

[0049] Step 202: Based on the device type, determine a target image acquisition algorithm corresponding to the device type from preset image acquisition algorithms;

[0050] In one embodiment, step 202 may be specifically implemented as follows: using a preset correspondence between device types and image acquisition algorithms, determining a target image acquisition algorithm corresponding to the device type from among the image acquisition algorithms.

[0051] Figure 3 is a schematic diagram of the correspondence between device type and image acquisition algorithm. For example, if the device type is type a, then Figure 3 The correspondence between the device type and the image acquisition algorithm can determine that the image acquisition algorithm corresponding to type a is image acquisition algorithm 1.

[0052] The image acquisition algorithm in the embodiment of the present application can be set according to the specific actual situation, and the embodiment of the present application does not limit the image acquisition algorithm.

[0053] Step 203: Based on the eye tracking algorithm identifier, determine a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from preset eye tracking algorithms;

[0054] In one embodiment, step 203 can be specifically implemented in the following two ways:

[0055] Method 1: If there is a plurality of eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms, an eye tracking algorithm with the highest priority among the eye tracking algorithms corresponding to the eye tracking algorithm identifier is determined as a target eye tracking algorithm corresponding to the eye tracking algorithm identifier.

[0056] In the embodiment of the present application, the priority of each eye tracking algorithm is pre-configured. For example, each eye tracking algorithm corresponding to the eye tracking algorithm identifier includes eye tracking algorithm 1, eye tracking algorithm 2, and eye tracking algorithm 3. And the priority between each eye tracking algorithm is: eye tracking algorithm 2> eye tracking algorithm 2> eye tracking algorithm 3, then it is determined that the target eye tracking algorithm corresponding to the eye tracking identifier is eye tracking algorithm 2.

[0057] Method 2: If the number of eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms is one, then the eye tracking algorithm corresponding to the eye tracking algorithm identifier is determined as the target eye tracking algorithm.

[0058] In one embodiment, the eye tracking algorithm corresponding to the eye tracking algorithm identifier is obtained in the following manner: using a preset correspondence between the eye tracking algorithm identifier and the eye tracking algorithm, a target eye tracking algorithm corresponding to the eye tracking algorithm identifier is obtained from the eye tracking algorithms.

[0059] like Figure 4 As shown, Figure 4 Schematic diagram of the correspondence between the eye tracking algorithm identifier and the eye tracking algorithm. If the eye tracking algorithm identifier is A, the corresponding eye tracking algorithms are determined to be eye tracking algorithm 1 and eye tracking algorithm 2.

[0060] Step 204: acquiring image data using the target image acquisition algorithm at specified time intervals;

[0061] The specified time length in the embodiment of the present application can be 0.1 seconds, 1 second, 2 seconds, etc., and can be set specifically according to actual conditions. The embodiment of the present application does not limit the specific value of the specified time length.

[0062] In order to save computing resources, in one embodiment, if it is determined that the eye movement function has been turned off, the step of acquiring image data using the target image acquisition algorithm at specified time intervals is stopped.

[0063] Step 205: Send the image data to the target eye movement tracking algorithm to obtain eye movement data corresponding to the image data.

[0064] like Figure 5The figure is a schematic diagram of the corresponding process when the eye movement data is not obtained, which may specifically include the following steps:

[0065] Step 501: if the target eye tracking algorithm cannot obtain eye movement data, determining whether there are multiple eye tracking algorithms corresponding to the eye tracking algorithm identifier in the preset eye tracking algorithms, if so, executing step 502, if not, executing step 504;

[0066] Step 502: Determine an updated target eye tracking algorithm by using the priorities of other eye tracking algorithms except the target eye tracking algorithm;

[0067] Step 503: sending the image data to the updated target eye tracking algorithm to obtain eye movement data corresponding to the image data;

[0068] In the embodiment of the present application, the eye tracking algorithm with the highest priority among other eye tracking algorithms except the target eye tracking algorithm is determined as the updated target eye tracking algorithm.

[0069] Step 504: Prompt the user that the eye movement function of the device cannot be used normally.

[0070] like Figure 6 As shown, it is a schematic diagram of the interface. When the eye movement data cannot be calculated, the user is reminded that the eye movement function of the device cannot be used normally and timely maintenance is carried out.

[0071] Next, the eye movement data determination method in the embodiment of the present application is introduced in conjunction with the system architecture diagram of the eye movement data determination method of the present application. Figure 7 , which is a system architecture diagram of a method for determining eye movement data, the system includes an eye movement tracking client 701, an eye movement tracking service 702, an image acquisition algorithm management service 703, and an eye movement tracking algorithm management service 704. Among them:

[0072] After the device is turned on, the eye tracking service 702 is started, and the application connects to the eye tracking service 702 through the eye tracking client 701. The eye tracking service 702 obtains a pre-stored configuration file, and then determines the target image acquisition algorithm corresponding to the device type from the preset image acquisition algorithms based on the device type; and determines the target eye tracking algorithm corresponding to the eye tracking algorithm identifier from the preset eye tracking algorithms based on the eye tracking algorithm identifier; starts the target image acquisition algorithm through the image acquisition algorithm management service 703 to acquire image data at specified intervals, and finally sends the image data to the target eye tracking algorithm through the eye tracking algorithm management service 704 to obtain eye movement data corresponding to the image data.

[0073] In order to further understand the method in this application, Figure 8 FIG. 1 is a flow chart of a method for determining eye movement data in the present application, which may include the following steps:

[0074] Step 801: when the eye movement function is started, a pre-stored configuration file is obtained, wherein the configuration file includes a device type and an eye movement tracking algorithm identifier, wherein the device type is used to identify the type corresponding to the eye movement hardware device in the device;

[0075] Step 802: Based on the device type, determine a target image acquisition algorithm corresponding to the device type from preset image acquisition algorithms;

[0076] Step 803: determining whether there are multiple eye tracking algorithms corresponding to the eye tracking algorithm identifier in the preset eye tracking algorithms, if so, executing step 804, if not, executing step 805;

[0077] Step 804: determining the eye tracking algorithm with the highest priority among the eye tracking algorithms corresponding to the eye tracking algorithm identifier as the target eye tracking algorithm corresponding to the eye tracking algorithm identifier;

[0078] Step 805: determining the eye tracking algorithm corresponding to the eye tracking algorithm identifier as the target eye tracking algorithm;

[0079] Step 806: acquiring image data using the target image acquisition algorithm at specified time intervals;

[0080] Step 807: Send the image data to the target eye tracking algorithm to obtain eye movement data corresponding to the image data.

[0081] Based on the same inventive concept, the eye movement data determination method disclosed above can also be implemented by an eye movement data determination device. The effect of the eye movement data determination device is similar to that of the above method, and will not be repeated here.

[0082] Fig. 9 The figure is a schematic diagram of the structure of a device for determining eye movement data according to an embodiment of the present disclosure.

[0083] like Fig. 9 As shown, the eye movement data determination device 900 of the present disclosure may include an acquisition module 910 , a first algorithm determination module 920 , a second algorithm determination module 930 , an image acquisition module 940 and an eye movement data determination module 950 .

[0084] An acquisition module 910 is used to acquire a pre-stored configuration file after the eye movement function is started, wherein the configuration file includes a device type and an eye movement tracking algorithm identifier, wherein the device type is used to identify the type corresponding to the eye movement hardware device in the device;

[0085] A first algorithm determination module 920 is used to determine a target image acquisition algorithm corresponding to the device type from preset image acquisition algorithms based on the device type; and

[0086] A second algorithm determination module 930 is used to determine a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from preset eye tracking algorithms based on the eye tracking algorithm identifier;

[0087] The image acquisition module 940 is used to acquire image data using the target image acquisition algorithm at specified time intervals; and

[0088] The eye movement data determination module 950 is used to send the image data to the target eye movement tracking algorithm to obtain eye movement data corresponding to the image data.

[0089] In one embodiment, the second algorithm determination module 930 is specifically used to:

[0090] If there are multiple eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms, then the eye tracking algorithm with the highest priority among the eye tracking algorithms corresponding to the eye tracking algorithm identifier is determined as the target eye tracking algorithm corresponding to the eye tracking algorithm identifier; or,

[0091] If the number of eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms is one, the eye tracking algorithm corresponding to the eye tracking algorithm identifier is determined as the target eye tracking algorithm.

[0092] In one embodiment, the second algorithm determination module 930 is further configured to:

[0093] By using the preset correspondence between the eye tracking algorithm identifier and the eye tracking algorithm, an eye tracking algorithm corresponding to the eye tracking algorithm identifier is obtained from the eye tracking algorithms.

[0094] In one embodiment, the first algorithm determination module 920 is specifically configured to:

[0095] By using the preset correspondence between the device type and the image acquisition algorithm, a target image acquisition algorithm corresponding to the device type is determined from the image acquisition algorithms.

[0096] In one embodiment, the apparatus further comprises:

[0097] A judgment module 960 is used for determining whether there are multiple eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms if the target eye tracking algorithm cannot obtain the eye tracking data after the image data is sent to the target eye tracking algorithm;

[0098] an updating module 970, configured to, if yes, determine an updated target eye tracking algorithm by using the priorities of other eye tracking algorithms except the target eye tracking algorithm, and send the image data to the updated target eye tracking algorithm to obtain eye movement data corresponding to the image data;

[0099] The reminder module 980 is used to remind the user that the eye movement function of the device cannot be used normally if no.

[0100] In one embodiment, the apparatus further comprises:

[0101] The closing module 990 is used to stop executing the step of acquiring image data by using the target image acquisition algorithm every specified time period if it is determined that the eye movement function has been turned off.

[0102] After introducing a method and device for determining eye movement data according to an exemplary embodiment of the present invention, next, an electronic device according to another exemplary embodiment of the present invention is introduced, and the electronic device can be a VR device and an AR device.

[0103] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0104] In some possible implementations, the electronic device according to the present invention may include at least one processor and at least one computer storage medium. The computer storage medium stores program code, and when the program code is executed by the processor, the processor executes the steps of the method for determining eye movement data according to various exemplary embodiments of the present invention described above in this specification. For example, the processor may execute the following steps: Figure 2 Steps 201-205 shown in .

[0105] Refer to the following Fig.10 The electronic device 1000 according to this embodiment of the present invention will be described. Fig.10The electronic device 1000 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0106] like Fig.10 As shown, the electronic device 1000 is presented in the form of a general AR device. The components of the electronic device 1000 may include but are not limited to: the at least one processor 1001, the at least one computer storage medium 1002, and a bus 1003 connecting different system components (including the computer storage medium 1002 and the processor 1001).

[0107] Bus 1003 represents one or more of several types of bus structures, including a computer storage media bus or computer storage media controller, a peripheral bus, a processor, or a local bus using any of a variety of bus architectures.

[0108] The computer storage medium 1002 may include readable media in the form of volatile computer storage media, such as random access computer storage media (RAM) 1021 and / or cache storage media 1022 , and may further include read-only computer storage media (ROM) 1023 .

[0109] The computer storage medium 1002 may also include a program / utility 1025 having a set (at least one) of program modules 1024, such program modules 1024 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.

[0110] The electronic device 1000 may also communicate with one or more external devices 1004 (e.g., keyboards, pointing devices, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 1000, and / or communicate with any device that enables the electronic device 1000 to communicate with one or more other AR devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 1005. In addition, the electronic device 1000 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 1006. As shown, the network adapter 1006 communicates with other modules for the electronic device 1000 through a bus 1003. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0111] In some possible implementations, various aspects of the method for determining eye movement data provided by the present invention may also be implemented in the form of a program product, which includes program code. When the program product runs on a computer device, the program code is used to enable the computer device to execute the steps of the method for determining eye movement data according to various exemplary embodiments of the present invention described above in this specification.

[0112] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for determining eye movement data, characterized in that: The method comprises: When the eye tracking function is started, a pre-stored configuration file is obtained, wherein the configuration file includes a device type and an eye tracking algorithm identifier, wherein the device type is used to identify the type corresponding to the eye tracking hardware device in the device; Based on the device type, determining a target image acquisition algorithm corresponding to the device type from preset image acquisition algorithms; and, Based on the eye tracking algorithm identifier, determining a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from preset eye tracking algorithms; At specified time intervals, the target image acquisition algorithm is used to acquire image data; and, The image data is sent to the target eye tracking algorithm to obtain eye movement data corresponding to the image data.

2. The method according to claim 1, characterized in that The step of determining a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from preset eye tracking algorithms based on the eye tracking algorithm identifier includes: If there are multiple eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms, then the eye tracking algorithm with the highest priority among the eye tracking algorithms corresponding to the eye tracking algorithm identifier is determined as the target eye tracking algorithm corresponding to the eye tracking algorithm identifier; or, If the number of eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms is one, the eye tracking algorithm corresponding to the eye tracking algorithm identifier is determined as the target eye tracking algorithm.

3. The method according to claim 2, characterized in that The eye tracking algorithm corresponding to the eye tracking algorithm identifier is obtained by: By using the preset correspondence between the eye tracking algorithm identifier and the eye tracking algorithm, an eye tracking algorithm corresponding to the eye tracking algorithm identifier is obtained from the eye tracking algorithms.

4. The method according to claim 1, characterized in that The step of determining a target image acquisition algorithm corresponding to the device type from preset image acquisition algorithms based on the device type includes: By using the preset correspondence between the device type and the image acquisition algorithm, a target image acquisition algorithm corresponding to the device type is determined from the image acquisition algorithms.

5. The method according to claim 1, characterized in that After sending the image data to the target eye tracking algorithm, the method further includes: If the target eye tracking algorithm cannot obtain the eye movement data, determining whether there is more than one eye tracking algorithm among the preset eye tracking algorithms corresponding to the eye tracking algorithm identifier; If so, determining an updated target eye tracking algorithm by using the priorities of other eye tracking algorithms except the target eye tracking algorithm, and sending the image data to the updated target eye tracking algorithm to obtain eye movement data corresponding to the image data; If not, the user is prompted that the eye movement function of the device cannot be used normally.

6. The method according to claim 1, characterized in that The method further comprises: If it is determined that the eye movement function has been turned off, the step of acquiring image data using the target image acquisition algorithm at specified time intervals is stopped.

7. An electronic device, characterized in that: comprising a processor and a memory, wherein the processor and the memory are connected via a bus; The memory stores a computer program, and the processor is configured to perform the following operations based on the computer program: When the eye tracking function is started, a pre-stored configuration file is obtained, wherein the configuration file includes a device type and an eye tracking algorithm identifier, wherein the device type is used to identify the type corresponding to the eye tracking hardware device in the device; Based on the device type, determining a target image acquisition algorithm corresponding to the device type from preset image acquisition algorithms; and, Based on the eye tracking algorithm identifier, determining a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from preset eye tracking algorithms; At specified time intervals, the target image acquisition algorithm is used to acquire image data; and, The image data is sent to the target eye tracking algorithm to obtain eye movement data corresponding to the image data.

8. The electronic device according to claim 7, characterized in that: The processor executes the step of determining, based on the eye tracking algorithm identifier, a target eye tracking algorithm corresponding to the eye tracking algorithm identifier from among various preset eye tracking algorithms, and is specifically configured as follows: If there is more than one eye tracking algorithm corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms, determining the eye tracking algorithm with the highest priority among the eye tracking algorithms corresponding to the eye tracking algorithm identifier as the target eye tracking algorithm corresponding to the eye tracking algorithm identifier; or, If the number of eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms is one, the eye tracking algorithm corresponding to the eye tracking algorithm identifier is determined as the target eye tracking algorithm.

9. The electronic device according to claim 7, characterized in that: The processor is further configured to: After sending the image data to the target eye tracking algorithm, if the target eye tracking algorithm cannot obtain the eye tracking data, determining whether there is a plurality of eye tracking algorithms corresponding to the eye tracking algorithm identifier among the preset eye tracking algorithms; If so, determining an updated target eye tracking algorithm by using the priorities of other eye tracking algorithms except the target eye tracking algorithm, and sending the image data to the updated target eye tracking algorithm to obtain eye movement data corresponding to the image data; If not, the user is prompted that the eye movement function of the device cannot be used normally.

10. The electronic device according to claim 7, characterized in that: The processor is further configured to: If it is determined that the eye movement function has been turned off, the step of acquiring image data using the target image acquisition algorithm at specified time intervals is stopped.

Images