An asynchronous feature tracking method and system based on events and frames

Abstract

This application provides an asynchronous feature tracking method and system based on events and frames. The method includes: a feature block initialization method based on FAST corner detection; an optical flow and affine transformation optimization method based on event information integration to update the feature block position; an optimization result evaluation method based on historical loss function values ​​to verify the effectiveness of the optical flow and affine transformation; and a feature block association method based on nearest neighbor search to determine new feature block initialization. The method is evaluated on a public dataset, with the evaluation metric being the time interval between feature initialization and feature loss, i.e., feature time, reflecting the robustness of the tracker. Compared to the original method, this invention improves feature time by approximately 10% to 30% while maintaining tracking accuracy, laying the foundation for computer vision tasks such as visual odometry.

Description

Technical Field

[0001] This application relates to the field of feature tracking technology, and in particular to a method and system for asynchronous feature tracking using events and frames (AFTEF). Background Technology

[0002] Dynamic vision sensors, as an emerging type of bio-inspired event camera, have attracted the interest of researchers in robotics and computer vision. Unlike traditional cameras that output absolute brightness frames, event cameras output an asynchronous stream of events that react to changes in the brightness of local pixels. Event information includes timestamps, polarity, and pixel coordinates. Advantages of event cameras include low power consumption, high dynamic range, and high temporal resolution. Furthermore, event cameras are sensitive to scene motion and can reflect brightness changes corresponding to each pixel with low latency (1 μs). Another bio-inspired sensor, dynamic and active pixel vision sensors, can provide asynchronous event streams and brightness frames.

[0003] Feature detection and tracking is a crucial component of feature-based visual odometry. Because the asynchronous event stream of an event camera differs from that of a luminance frame, frame-based feature detection and tracking algorithms cannot be directly applied to asynchronous event streams. Therefore, new algorithms need to be explored to handle such asynchronous event streams and unlock their potential. In this context, leveraging the complementary information between event cameras and traditional cameras is a key challenge for feature detection and tracking.

[0004] In the field of feature detection for event cameras, current research largely involves extensions and fusions of popular image-based corner detection methods. One proposed frame-based extended Harris corner detection method detects corners on binary frames obtained from event accumulation, requiring gradient and convolution computation. Inspired by the frame-based FAST (Features from Accelerated Segment Test) corner detection method, an event-based method called eFAST has been proposed, performing detection on the Surface of Active Events (SAE), requiring only comparison operations. The SAE is a two-dimensional representation of the event stream, storing the timestamp of the most recent event at each pixel location. To improve the robustness of eFAST, an event feature detection method based on SAE filtering, called Arc*, has been proposed. This algorithm detects corners faster than eFAST and eHarris while enhancing the repeatability of corner detection. FA-Harris provides a selection and refinement strategy, using an improved eFAST to select candidate points and then filtering them with an improved eHarris. A method consisting of a three-layer filtering stage and low-complexity Harris detection has been proposed.

[0005] In the field of feature tracking using event cameras, researchers are dedicated to implementing event-driven asynchronous feature tracking methods by leveraging event information. One approach proposes a probabilistic association method between event streams and features, describing event corner tracking as an optimization problem of matching the current view with a feature template, requiring only the evaluation of a set of discrete tracking hypotheses. Another approach proposes a tree-structured event feature tracking method with multiple data association possibilities, where each node is an event corner, and the matching mechanism for adding nodes is based on spatiotemporal constraints. Based on spatiotemporal constraints, constraints on the direction of corner points have been added to the tree structure. This improvement can prune branches and simplify the tree structure. A gradient descriptor based on a velocity-invariant time surface has been proposed and used as the basis for matching two event corners in the tree structure. A first feature tracking method using both frame and event cameras has been proposed, where feature blocks are initialized on the frame using corner and edge detectors, and then aligned using the event stream via a two-dimensional Euclidean transformation to achieve tracking.

[0006] However, the methods described above do not fully utilize the advantages of event information. Specifically, event information includes not only spatial and temporal information but also polarity information. These methods only utilize spatiotemporal information and do not utilize polarity information. Furthermore, these methods do not use information from traditional cameras as a supplement.

[0007] A better combination of frame and event cameras is the use of the asynchronous photometric feature tracking (EKLT) method, which detects features on the luminance frame and then tracks them using an event stream. This achieves asynchronous tracking and fully utilizes the polarity information of the event stream. Summary of the Invention

[0008] In view of this, the purpose of this application is to propose an asynchronous feature tracking method and system based on events and frames, which can specifically solve the existing problems.

[0009] To achieve the above objectives, this application proposes an asynchronous feature tracking method based on events and frames, comprising:

[0010] Step 1: Use the decision tree-based FAST corner detection method on the frame and use non-maximum suppression to obtain the initialized feature points;

[0011] Step 2: Extract feature blocks from the image plane surrounding the feature points;

[0012] Step 3: Establish an observation model for events generated by the event camera;

[0013] Step 4: Using the observation model, integrate the polarity information of the event flow to obtain the observed values ​​of the photometric increment image;

[0014] Step 5: Based on the principle of photometric invariance of local feature blocks, the predicted value of the photometric increment image is obtained from the image gradient, optical flow and affine transformation;

[0015] Step 6: Establish a loss function based on the difference between two normalized photometric increment images, and use a nonlinear optimization method to minimize the loss function to obtain the motion parameters of the feature block;

[0016] Step 7: Evaluate the effectiveness of the motion parameters based on the historical values ​​of the loss function;

[0017] Step 8: Update the position of the feature block using optical flow and affine transformation;

[0018] Step 9: After initializing the new feature points, traverse all existing feature blocks, find the nearest initial feature point based on the Euclidean distance, and ensure that its distance is below the threshold.

[0019] Step 10: Extract new feature blocks centered on the unmatched initial feature points.

[0020] Furthermore, the FAST corner detection method in step 1 is a frame-based corner detection method.

[0021] Furthermore, the event camera in step 3 is a bionic vision sensor that asynchronously outputs discrete information, and its output information is called an event, including image plane coordinates, timestamps, and polarity.

[0022] Furthermore, in step 4, the event polarity information is integrated, and the polarity information of events whose positions fall within the feature block is accumulated.

[0023] Furthermore, the principle of local feature block luminance invariance in step 5 assumes that the local luminance of the image plane remains unchanged for an extremely short time, the image gradient is the difference in luminance value in two directions of the image plane, the optical flow is the velocity of a point moving in the image plane, and the affine transformation is the rotation and translation matrix of a two-dimensional plane.

[0024] Furthermore, the Euclidean distance in step 9 is the distance between two two-dimensional points on the image plane.

[0025] To achieve the above objectives, this application also proposes an asynchronous feature tracking system based on events and frames, comprising:

[0026] The initialization module initializes the feature blocks of the frame based on FAST corner detection.

[0027] The update module updates the feature block positions based on optical flow and affine transformation optimization methods using event information integration.

[0028] The verification module verifies the effectiveness of optical flow and affine transformation based on the optimization result evaluation method using historical loss function values.

[0029] The association module determines new feature block initialization based on the feature block association method of nearest neighbor search.

[0030] In summary, the advantages of this application and the user experience it provides are as follows: this method can improve tracking time while maintaining tracking accuracy. Compared with existing methods, the overall trajectory quality is improved, feature time delay is reduced, and tracking accuracy is guaranteed. This invention provides the necessary foundation for the future development of fully event-driven visual odometry and other computer vision tasks. Attached Figure Description

[0031] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0032] Figure 1 A flowchart illustrating an asynchronous feature tracking method based on events and frames according to an embodiment of this application is shown.

[0033] Figure 2A schematic diagram of the original FAST corner detection method is shown.

[0034] Figure 3 A visual diagram illustrating event flow information is shown.

[0035] Figure 4 A visual representation of the dataset is shown.

[0036] Figure 5 This is a visual illustration of the dataset tracking.

[0037] Figure 6 The diagram shown is a comparison of the average tracking errors of the AFTEF method and the EKLT method in this application.

[0038] Figure 7 The diagram shown is a comparison of the feature times of the AFTEF method and the EKLT method in this application.

[0039] Figure 8 A schematic diagram of an event- and frame-based asynchronous feature tracking system according to an embodiment of this application is shown.

[0040] Figure 9 A schematic diagram of the structure of an electronic device provided in one embodiment of this application is shown.

[0041] Figure 10 A schematic diagram of a storage medium provided in one embodiment of this application is shown. Detailed Implementation

[0042] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0043] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0044] Figure 1 A flowchart illustrating an asynchronous feature tracking method based on events and frames according to an embodiment of this application is shown. Figure 1 As shown, this event- and frame-based asynchronous feature tracking method includes:

[0045] Step 1: Apply the FAST corner detection method based on decision trees to the frame, through... Figure 2 The brightness values ​​of pixels on the arc shown are used to detect corner points, and non-maximum suppression is used to obtain initialized feature points.

[0046] Step 2: Extract feature blocks from the image plane surrounding the feature points;

[0047] Step 3: Establish an observation model for events generated by the event camera;

[0048] like Figure 3 As shown in the image plane and time axis, events are discrete information, containing only spatial, temporal, and polarity information. Specifically, an event {u, pol, t} includes the two-dimensional position of the pixel u = {x, y}, representing the polarity of the brightness change pol ∈ {+1, -1}, and the timestamp t that triggered the event. An event occurs when the logarithmic brightness change of the pixel position between t and t-Δt exceeds a threshold ±C (C > 0). q(u, t) = log(I(u, t)) is the logarithmic brightness image, and the logarithmic brightness increment image Δq(u, t) can be represented as:

[0049] Δq(u,t)=q(u,t)-q(u,t-Δt)=pol*C (1)

[0050] Where t-Δt is the timestamp of the previous event at the same pixel location.

[0051] Step 4: Using the observation model, integrate the polarity information of the event flow to obtain the observed values ​​of the photometric increment image;

[0052] When the number of events falling into feature block P reaches the adaptive threshold N e At that time, the event polarity at each pixel location in the feature block is accumulated within the time interval Δτ. As shown in Equation (2), Δq(u,t) is called the observation value of the photometric increment image:

[0053]

[0054] Where f(u, t) represents the polarity of all events at timestamp t at pixel location u. Note that the polarity is -1 or +1, and the events are generated based on a threshold C. The photometric increment image is then normalized. Adaptive threshold N e It is initialized to a constant value (e.g., 100), and subsequently adjusted based on the image gradient. After optimizing and recalculating the optical flow v:

[0055]

[0056] Step 5: Based on the principle of local photometric invariance, the predicted value of the photometric increment image is obtained from the image gradient, optical flow, and affine transformation;

[0057] Consider the case where the optical flow is unknown. Assume that the gradient and logarithmic brightness image in the feature block are constant over a certain time interval, q(u,t) = const, and the derivative of q(u,t) is expressed as:

[0058]

[0059] in, Let be the brightness gradient at the pixel location, and v be the optical flow. The Taylor approximation of Equation 4 can be expressed as:

[0060]

[0061] Substitute equation 4 into equation 5:

[0062]

[0063] The gradient is referred to as the predicted value of the photometric increment image because the optical flow is unknown. In fact, the gradient changes with the motion parameter p:

[0064] W(u, p) = R(p) * u + t(p) (7)

[0065] Where (R, t) ∈ SE(2) are rotation and translation quantities represented by the Lie group, and p ∈ se(2) are the corresponding Lie algebras. After performing an affine transformation on the gradient, the predicted value of the photometric increment image can be written as:

[0066]

[0067] Step 6: Establish a loss function based on the difference between the two normalized photometric increment images, and use a nonlinear optimization method to minimize the loss function to obtain the motion parameters (optical flow and affine transformation) of the feature block;

[0068] The loss function V is written as:

[0069]

[0070] Step 7: Evaluate the effectiveness of motion parameters based on historical values ​​of the loss function;

[0071] The motion parameters after each optimization are p last and v last The corresponding final loss function value is V last :

[0072]

[0073] Take the final loss function values ​​of the feature block from the most recent n optimizations, and calculate the average value:

[0074]

[0075] The quality of the optimization results is based on V average With threshold c thresholdThe evaluation is based on the magnitude of the compared value. The threshold is a constant parameter preset based on experience. If it exceeds the threshold c... threshold If the optimization fails, the feature block's state is set to "missing". Conversely, if the optimization succeeds, the feature block's position is updated.

[0076] Step 8: Update the position of the feature block using optical flow and affine transformation;

[0077] The optimized motion parameters (optical flow v and affine transformation p) are used to update the feature block positions:

[0078] u′=R(p) -1 *uR(p) -1 *t(p) (12)

[0079] Step 9: After initializing the new feature points, traverse all existing feature blocks, find the nearest initial feature point based on the Euclidean distance, and ensure that its distance is below the threshold.

[0080] Step 10: Extract new feature blocks centered on the unmatched initial feature points.

[0081] The algorithm is in Figure 4 The validation was performed on the displayed dataset scenes: (a) a simple black and white scene, (b) and (c) high-texture scenes, and (d) and (e) natural scenes. A schematic diagram of the trajectory obtained by tracking the feature blocks is shown below. Figure 5 As shown, the clustered short lines represent the trajectory of the feature blocks. Figure 6 The figure shows a comparison of the average tracking errors of the AFTEF method and the EKLT method in this application. Figure 7 The figure shows a comparison of the feature times of the AFTEF method and the EKLT method in this application. Figure 6 and Figure 7 The results show that, compared with the EKLT method, the AFTEF method proposed in this application improves the feature tracking time of the overall feature block by about 10% to 30% while ensuring the accuracy of feature tracking.

[0082] The application provides an asynchronous feature tracking system based on events and frames. This system is used to execute the asynchronous feature tracking method based on events and frames described in the above embodiments, such as... Figure 8 As shown, the system includes:

[0083] Initialization module 501 initializes the feature blocks of the frame based on FAST corner detection;

[0084] Update module 502 updates the feature block position based on optical flow and affine transformation optimization methods using event information integration;

[0085] Validation module 503 verifies the effectiveness of optical flow and affine transformation based on the optimization result evaluation method using historical loss function values;

[0086] The association module 504 determines the initialization of new feature blocks based on the feature block association method of nearest neighbor search.

[0087] The event- and frame-based asynchronous feature tracking system provided in the above embodiments of this application and the event- and frame-based asynchronous feature tracking method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0088] This application also provides an electronic device corresponding to the event- and frame-based asynchronous feature tracking method provided in the foregoing embodiments, for executing the event- and frame-based asynchronous feature tracking method. This application does not limit the scope of the embodiments.

[0089] Please refer to Figure 9 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 9 As shown, the electronic device 20 includes: a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected via the bus 202. The memory 201 stores a computer program that can run on the processor 200. When the processor 200 runs the computer program, it executes the asynchronous feature tracking method based on events and frames provided in any of the foregoing embodiments of this application.

[0090] The memory 201 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 203 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0091] Bus 202 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Memory 201 is used to store programs. After receiving an execution instruction, the processor 200 executes the program. The asynchronous feature tracking method based on events and frames disclosed in any of the foregoing embodiments of this application can be applied to the processor 200, or implemented by the processor 200.

[0092] The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 200 or by instructions in software form. The processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 201. The processor 200 reads the information in memory 201 and, in conjunction with its hardware, completes the steps of the above method.

[0093] The electronic device provided in this application embodiment and the asynchronous feature tracking method based on events and frames provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, run or implement.

[0094] This application also provides a computer-readable storage medium corresponding to the event- and frame-based asynchronous feature tracking method provided in the foregoing embodiments. Please refer to [link / reference]. Figure 10 The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the event- and frame-based asynchronous feature tracking method provided in any of the foregoing embodiments.

[0095] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0096] The computer-readable storage medium provided in the above embodiments of this application and the asynchronous feature tracking method based on events and frames provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0097] It should be noted that:

[0098] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0099] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0100] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0101] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0102] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0103] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation system according to the embodiments of this application. This application can also be implemented as a device or system program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0104] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0105] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An event and frame based asynchronous feature tracking method, characterized in that, The method comprises the following steps: Step 1, using a FAST corner detection method based on a decision tree on a frame, and using non-maximum suppression to obtain initialized feature points; Step 2, extracting a feature block in an image plane around the feature points; Step 3, establishing an observation model of event generation of an event camera; Step 4, using the observation model, integrating polarity information of an event stream to obtain an observation value of a luminance increment image; the polarity information of the event stream is integrated to accumulate polarity information of events whose positions fall into the feature block; Step 5, based on a local feature block luminance invariance principle, obtaining a prediction value of the luminance increment image from image gradients, optical flow and affine transformation; Step 6, establishing a loss function based on a difference value of two normalized luminance increment images, and using a nonlinear optimization method to minimize the loss function to obtain motion parameters of the feature block, the motion parameters being optical flow and affine transformation; Step 7, evaluating based on historical values of the loss function to judge validity of the motion parameters; Step 8, updating the position of the feature block using the optical flow and the affine transformation; Step 9, after initializing new feature points, traversing all existing feature blocks, finding the nearest initial feature points according to Euclidean distance, and ensuring that the distance is lower than a threshold value; Step 10, extracting new feature blocks centered on the initialized feature points that are not matched.

2. The event and frame based asynchronous feature tracking method of claim 1, wherein: The FAST corner detection method in the step 1 is a frame-based corner detection method.

3. The event and frame based asynchronous feature tracking method of claim 1, wherein: The event camera in the step 3 is a bionic visual sensor that outputs discrete information asynchronously, and the output information is called an event, including an image plane coordinate, a timestamp and polarity.

4. The event and frame based asynchronous feature tracking method of claim 1, wherein: The local feature block luminance invariance principle in the step 5 assumes that the local luminance of the image plane is invariant in a very short time, the image gradient is a difference value of luminance values in two directions of the image plane, the optical flow is a motion speed of a point in the image plane, and the affine transformation is a rotation and translation matrix of a two-dimensional plane.

5. The event and frame based asynchronous feature tracking method of claim 1, wherein: The Euclidean distance in the step 9 is a distance of two two-dimensional points in the image plane.

6. An event and frame based asynchronous feature tracking system for supporting the method of any one of claims 1-5, characterized in that, The method comprises: an initialization module that initializes feature blocks of a frame based on FAST corner detection; an updating module that updates the position of the feature block based on an optical flow and affine transformation optimization method of event information integration; a verification module that verifies validity of the optical flow and the affine transformation based on an optimization result evaluation method of historical loss function values; a correlation module that determines initialization of new feature blocks based on a nearest neighbor search feature block correlation method.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor runs the computer program to implement the method of any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-5.

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