Method and device for assessing and eliminating camera disturbance effect, and storage medium

Abstract

Disclosed in the present application are a method and device for assessing and eliminating a camera disturbance effect, and a storage medium. The method comprises: decomposing a signal to be processed, and then performing one-by-one removal, so as to generate a plurality of second signal sets; then obtaining a plurality of frequency-domain mirror image indicators on the basis of curve information, which is obtained by means of performing frequency-domain analysis on the plurality of second signal sets, and a mirror image indicator formula; and then determining a disturbance signal on the basis of a maximum frequency-domain mirror image indicator, and removing the disturbance signal, so as to obtain a disturbance-eliminated signal.

Description

Camera disturbance effect evaluation and elimination method, device and storage medium

[0001] Related applications

[0002] This application claims priority to Chinese patent application No. 202311829753.9 filed on December 28, 2023, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the field of image processing technology, and in particular to a method, device, and storage medium for evaluating and eliminating camera disturbance effects. Background Art

[0004] Vision-based vibration measurement techniques utilize cameras or sensors to capture the displacement of an object's surface under vibration. These techniques offer advantages such as high measurement accuracy, long monitoring distances, the absence of direct contact with the object being measured, and low monitoring costs. Compared to contact measurement methods, they offer a wider range of applications and technical advantages. However, vision-based vibration measurement techniques are inevitably subject to interference from external environmental vibration noise during measurement applications. This can cause camera perturbations during image data acquisition, which in turn affects the accuracy of the structural vibration time-history signals derived from image data analysis. Therefore, eliminating camera perturbations has become a pressing issue.

[0005] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art.

[0006] Summary of the Invention

[0007] The main purpose of this application is to provide a method, device and storage medium for evaluating and eliminating camera disturbance effects, aiming to solve the technical problem of eliminating camera disturbance effects.

[0008] To achieve the above objectives, the present application provides a method for evaluating and eliminating camera disturbance effects, the method comprising the following steps:

[0009] Performing signal decomposition on a signal to be processed to obtain a first signal set, wherein the signal to be processed is a signal obtained by analyzing a video captured by a camera;

[0010] Selecting a different signal from the first signal set each time to eliminate it, to obtain multiple second signal sets;

[0011] Performing frequency domain analysis on the plurality of second signal sets to obtain a curve information set;

[0012] determining a frequency domain mirror index set based on the curve information in the curve information set and a mirror index formula, wherein the frequency domain mirror index set includes a plurality of frequency domain mirror indices, and each frequency domain mirror index corresponds to a signal removed from the first signal set; and

[0013] A maximum frequency domain image index is determined from the multiple frequency domain image indexes, a disturbance signal in the signal to be processed is determined according to the maximum frequency domain image index, and the disturbance signal is eliminated to obtain a disturbance elimination signal.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a camera disturbance effect evaluation and elimination device, which includes: a memory, a processor, and a camera disturbance effect evaluation and elimination program stored on the memory and executable on the processor, wherein the camera disturbance effect evaluation and elimination program is configured to implement the steps of the camera disturbance effect evaluation and elimination method as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, on which a camera disturbance effect evaluation and elimination program is stored. When the camera disturbance effect evaluation and elimination program is executed by a processor, the steps of the camera disturbance effect evaluation and elimination method described above are implemented.

[0016] This application generates multiple second signal sets by decomposing the signal to be processed and then eliminating them one by one. Then, multiple frequency domain mirror indicators are obtained based on the curve information and mirror indicator formula obtained after frequency domain analysis of the multiple second signal sets. The disturbance signal is determined based on the maximum frequency domain mirror indicator, and the disturbance signal is eliminated to obtain a disturbance elimination signal, thereby improving the accuracy of vibration measurement and realizing the evaluation and elimination of camera disturbance effects. This application quantitatively evaluates the disturbance effect by determining the frequency domain mirror indicator, thereby determining the part of the vibration time history signal with severe disturbance effects, and eliminating this part to obtain an accurate vibration time history signal, thereby improving the accuracy of vibration measurement and realizing the evaluation and elimination of camera disturbance effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG1 is a schematic diagram of the structure of a device for evaluating and eliminating camera disturbance effects in a hardware operating environment according to an embodiment of the present application;

[0018] FIG2 is a flow chart of a first embodiment of a method for evaluating and eliminating camera disturbance effects of the present application;

[0019] FIG3 is a schematic diagram of a sub-flow diagram of the second embodiment of the camera disturbance effect evaluation and elimination method of the present application;

[0020] FIG4 is a schematic diagram of another sub-flow in the second embodiment of the camera disturbance effect evaluation and elimination method of the present application;

[0021] FIG5 is a schematic diagram of another sub-flow in the second embodiment of the camera disturbance effect evaluation and elimination method of the present application;

[0022] FIG6 is a schematic diagram of a sub-flow diagram of the second embodiment of the camera disturbance effect evaluation and elimination method of the present application;

[0023] FIG7 is a schematic diagram of a sub-flow diagram of the third embodiment of the camera disturbance effect evaluation and elimination method of the present application;

[0024] FIG8 is a structural block diagram of the first embodiment of the camera disturbance effect evaluation and elimination device of the present application. DETAILED DESCRIPTION

[0025] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0026] Refer to Figure 1, which is a schematic diagram of the structure of a camera disturbance effect evaluation and elimination device in the hardware operating environment involved in an embodiment of the present application.

[0027] As shown in Figure 1, the camera disturbance effect assessment and elimination device may include: a processor 1001, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. The user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may include a standard wired interface and a wireless interface. The memory 1005 may be a high-speed random access memory (RAM), a stable non-volatile memory (NVM), or a storage device independent of the aforementioned processor 1001.

[0028] Those skilled in the art will understand that the structure shown in FIG1 does not constitute a limitation on the camera disturbance effect evaluation and elimination device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0029] As shown in FIG1 , the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a camera disturbance effect evaluation and elimination program.

[0030] In the camera disturbance effect assessment and elimination device shown in Figure 1, the network interface 1004 is used to communicate data with the network server; the user interface 1003 is used to interact with the user; the processor 1001 and the memory 1005 in the camera disturbance effect assessment and elimination device of the present application can be set in the camera disturbance effect assessment and elimination device, and the camera disturbance effect assessment and elimination device calls the camera disturbance effect assessment and elimination program stored in the memory 1005 through the processor 1001 to execute the camera disturbance effect assessment and elimination method provided in the embodiment of the present application.

[0031] FIG2 is a flow chart of a first embodiment of the camera disturbance effect evaluation and elimination method of the present application. In this embodiment, the camera disturbance effect evaluation and elimination method includes the following steps:

[0032] Step S1: performing signal decomposition on a signal to be processed to obtain a first signal set, wherein the signal to be processed is a signal obtained by analyzing a video captured by a camera;

[0033] It should be noted that the execution entity of the method of this embodiment can be a computing service device with data processing, network communication, and program execution capabilities, such as a mobile phone, tablet computer, personal computer, etc., or other electronic devices capable of performing the same or similar functions. The camera disturbance effect assessment and elimination device described above is used here to specifically illustrate the camera disturbance effect assessment and elimination methods provided in this embodiment and the following embodiments.

[0034] The signal to be processed is a displacement time-history signal obtained by performing a displacement time-history analysis on the object being measured in the video captured by the camera. The displacement time-history analysis refers to recording and analyzing the relationship between the displacement of the object being measured and time.

[0035] In one embodiment, before performing signal decomposition on the signal to be processed, it is necessary to first obtain the video captured by the camera, then perform displacement time history analysis on the captured video, and use the analysis result as the signal to be processed. The principle of signal decomposition is to decompose a signal into multiple local functions, each local function corresponding to a frequency and amplitude. The multiple local functions obtained after performing signal decomposition on the signal to be processed are used as the first signal set. Decomposing a displacement time history signal into multiple local functions makes the displacement time history signal analysis more accurate, which is beneficial for the subsequent determination of the displacement caused by camera perturbation and improves the effect of eliminating the camera perturbation effect.

[0036] In one embodiment, before performing signal decomposition on the signal to be processed, an abnormal time-history signal discrimination model trained by a large number of vibration time-history signals is constructed, and then the signal to be processed is discriminated using the abnormal time-history signal discrimination model, and the abnormalities of the signal to be processed are eliminated according to the processing results, and then the signal to be processed after the abnormalities are eliminated is decomposed to obtain a first signal set. By performing preliminary abnormality elimination on the signal to be processed, the accuracy of subsequent curve information can be improved, the frequency domain mirror indicator can be made more accurate, and the disturbance signal can be more accurately divided.

[0037] Step S2: selecting a different signal from the first signal set each time and eliminating it to obtain multiple second signal sets;

[0038] Step S3: performing frequency domain analysis on the plurality of second signal sets to obtain a curve information set;

[0039] First, delete the first signal in the first signal set, and then use the first signal set after deleting the signal as the second signal set. Delete the second signal in the first signal set for the second time, and then use the first signal set after deleting the signal as the second signal set. Repeat the above operation to obtain multiple second signal sets. It should be noted that there is no restriction on the order of deletion. It is only necessary to delete the signals that have not been deleted in the first signal set. For example, if the first signal deleted in the first signal set is a second signal set, then the signal deleted for the second time can be any signal other than the first signal in the first signal set, thereby obtaining another second signal set. Furthermore, the number of second signal sets obtained depends on the number of signals in the first signal set. The number of deletion operations performed is equal to the number of signals in the first signal set, thereby obtaining the same number of second signal sets.

[0040] Frequency domain analysis is performed on each second signal set using Formula 1, which is:

[0041] Where x(t) is the vibration time history signal of the tth discrete point in the time domain, i is the frequency domain discrete point signal corresponding to t, N is the time domain length of the signal; f(k) is the frequency domain vector of the structural vibration time history signal.

[0042] The time-domain signal in the second signal set is converted into a frequency-domain signal using Formula 1. This is achieved by decomposing the signal in the second signal set into a series of sinusoidal waves of varying frequencies, thereby representing the signal's characteristics in the frequency domain. Furthermore, Formula 1 allows a time-domain signal to be represented as the sum of a series of complex numbers, each representing the amplitude and phase of a sinusoidal wave of varying frequencies. These complex numbers constitute the frequency-domain representation of the signal.

[0043] The signals in the second signal set can be represented as a composite of sinusoidal signals of varying frequencies. The mathematical model for the relationship between the steady-state output and input signal of the signals in the second signal set when acted upon by sinusoidal functions of varying frequencies is the frequency characteristic, which is the complex ratio of the frequency response of the second signal set to the sinusoidal input signal when the second signal set is frequency-domain transformed. This frequency characteristic allows for linear analysis of the second signal set. Frequency-domain analysis of multiple second signal sets involves calculating the proportion of sinusoidal waves of varying frequencies within the signal.

[0044] In one embodiment, when performing frequency domain analysis on the second signal set, some sinusoidal wave components with relatively large amplitudes may be retained for future signal recovery. This approach has numerous practical benefits, such as reducing the data required to represent the signal, saving memory for storing data, saving data transmission time, and increasing the efficiency of communication lines. By performing frequency domain analysis on multiple second signal sets, multiple second signal sets can be analyzed from a frequency perspective. After obtaining multiple frequency spectra, curve information sets can be generated, enabling dynamic analysis of multiple second signal sets. This facilitates identifying signal components with significant camera perturbation effects, improving the accuracy and effectiveness of camera perturbation effect assessment and elimination.

[0045] S4: Determine a frequency domain mirror index set based on the curve information in the curve information set and a mirror index formula, wherein the frequency domain mirror index set includes multiple frequency domain mirror indices, and each frequency domain mirror index corresponds to a signal removed from the first signal set; the mirror index formula is:

[0046] Among them, · represents vector dot multiplication operation, || represents absolute value operation, × represents multiplication numerical operation; σ f and σ F denote the standard deviation of f(i) and the standard deviation of F(i) respectively; and Represent the average values ​​of f(i) and F(i) respectively. k Indicates the frequency domain image index for removing the k-th order modal signal.

[0047] The curve information set includes multiple curves, and the curve information includes the curve amplitude, curve shape, and curvature radius of the curve. Based on the curve amplitude, curve shape, and curvature radius of each curve, a frequency domain mirror index is obtained. This frequency domain mirror index can be used to evaluate and quantify the perturbation effect of the second signal set corresponding to the curve compared to the signal missing from the first signal set (generated by removing the signal from the first signal set). Furthermore, based on the curve amplitude, curve shape, and curvature radius of the multiple curves, multiple frequency domain mirror indexes are obtained to evaluate and quantify the perturbation effect of each signal in the first signal set.

[0048] In the above steps, the number of second signal sets obtained is the same as the number of signals included in the first signal set. Since each second signal set corresponds to a curve, the number of frequency domain mirror indicators in the frequency domain mirror indicator set is also the same as the number of signals included in the first signal set, and each frequency domain mirror indicator corresponds to a signal in the first signal set.

[0049] In one embodiment, a frequency domain mirror index set is determined by using curve information in a curve information set, thereby achieving evaluation and quantification of the disturbance effect on the signal, facilitating subsequent optimization of the disturbance effect, and improving the accuracy and efficiency of camera disturbance effect evaluation and elimination.

[0050] S5: Determine a maximum frequency domain image index from the multiple frequency domain image indexes, determine a disturbance signal in the signal to be processed according to the maximum frequency domain image index, and eliminate the disturbance signal to obtain a disturbance cancellation signal.

[0051] The maximum frequency domain mirror index is the maximum value among multiple frequency domain mirror indexes. The maximum value among multiple frequency domain mirror indexes is first determined, and then the range that needs to be eliminated is determined based on the preset order data. It should be noted that the preset order data is a numerical value set artificially according to different needs, and the range that needs to be eliminated refers to the part that is eliminated as a disturbance signal under different needs, that is, the disturbance signal. For example, if the preset order data is 10, then the signal corresponding to the maximum frequency domain mirror index and all signals of the 10 orders before the signal corresponding to the maximum frequency domain mirror index are used as disturbance signals and eliminated to obtain a disturbance elimination signal. Among them, within a certain range, the larger the preset order data, the larger the range that needs to be eliminated, that is, the better the effect of disturbance elimination. It should be understood that the preset order data cannot exceed the order of the signal to be processed to avoid total elimination.

[0052] In one embodiment, the disturbance signal is eliminated according to Formula 2, which is: x′(t)≈x(t)-∑u s (t)e iωst ,

[0053] Among them, x′(t) is the obtained disturbance elimination signal, x(t) is the signal to be processed, and u s (t) is the signal corresponding to the maximum frequency domain mirror index, s is the preset order data, and t is time.

[0054] Most camera disturbances are caused by external environmental vibration and noise, and the disturbance effects are difficult to identify with the human eye. Quantifying the disturbance effects through frequency domain mirror indicators can accurately identify areas in the signal where disturbance effects exist, thereby improving the accuracy and efficiency of camera disturbance effect assessment and elimination.

[0055] This application generates multiple second signal sets by decomposing the signal to be processed and then eliminating them one by one. Then, multiple frequency domain mirror indicators are obtained based on the curve information and mirror indicator formula obtained after frequency domain analysis of the multiple second signal sets. The disturbance signal is determined based on the maximum frequency domain mirror indicator, and the disturbance signal is eliminated to obtain a disturbance elimination signal, thereby improving the accuracy of vibration measurement and realizing the evaluation and elimination of camera disturbance effects. This application quantitatively evaluates the disturbance effect by determining the frequency domain mirror indicator, thereby determining the part of the vibration time history signal with severe disturbance effects, and eliminating this part to obtain an accurate vibration time history signal, thereby improving the accuracy of vibration measurement and realizing the elimination of camera disturbance effects.

[0056] Please refer to FIG. 3 , which is a schematic diagram of a sub-flow in the second embodiment of the camera disturbance effect evaluation and elimination method of the present application.

[0057] Based on the first embodiment above, in this embodiment, before step S1, the following steps are further included:

[0058] S1a: normalizing the video captured by the camera to obtain a normalized video;

[0059] S1b: Divide the normalized video into frames to obtain a divided frame set;

[0060] Normalization is a method of simplifying calculations by transforming a dimensionless expression into a scalar. Normalization makes incomparable data comparable while maintaining the relative relationship between the two compared data. The preset normalization template is obtained by normalizing the acceleration template. The acceleration template is obtained by attaching an accelerometer to the object being photographed and extracting the surface area of ​​the attachment. It is used to track the video captured by the camera.

[0061] In one embodiment, the video captured by the camera and the acceleration template are normalized using Formula 3 and Formula 4 respectively.

[0062] Formula 3 is:

[0063] The above formula 4 is:

[0064] Where w1 and h1 are the length and width of the acceleration template, w2 and h2 are the length and width of the video captured by the camera, T and I are the normalized template and the video captured by the camera, respectively. T' and I' are the normalized template and the video captured by the camera, respectively. It should be understood that videos are composed of frames, and the video to be processed is actually composed of multiple frames. Frame processing is performed on them to obtain a frame set.

[0065] By normalizing the video captured by the camera and the acceleration template, the dimensional influence of the two is eliminated, the data complexity is reduced, and the data visualization effect is improved, thereby improving the accuracy and efficiency of subsequent camera disturbance effect evaluation and elimination.

[0066] S1c: moving the preset normalized template on each frame in the frame set according to a first preset rule, and determining a similarity matrix according to a mapping value of each moving position to obtain multiple similarity matrices;

[0067] The first preset rule is to start from the vertex of each frame and move closely to the frame in a clockwise or counterclockwise direction until the entire frame is traversed. It should be noted that the order of movement can be from left to right, from top to bottom, or from left to right, from bottom to top, and there is no specific limitation here.

[0068] In one embodiment, the distance of each movement is one pixel, and the mapping value is the value of the position on the frame after each movement. Each time the normalized template moves through a frame, a similarity matrix is ​​calculated using Formula 5. Since there are multiple frames, a similarity matrix set is obtained. It should be understood that since a similarity matrix is ​​obtained each time a frame is moved through, the number of similarity matrices is the same as the number of frame sets. The above Formula 5 is:

[0069] Among them, (x, y) is the coordinate of a point on the normalized frame (normalized frames are obtained after the normalized video is framed); (x', y') is the normalized template coordinate, T(x, y) is the normalized template, and the normalized template is of size w×h; a point (x, y) on the similarity matrix R(x, y) represents the correlation between the image sub-block with (x, y) as the upper left corner point in the normalized frame I' and the same size as the template image T(x, y) and T(x, y).

[0070] S1d: reconstructing each similarity matrix in the plurality of similarity matrices to obtain a reconstructed matrix set;

[0071] S1e: Obtain the maximum value corresponding to each reconstruction matrix in the reconstruction matrix set to obtain a maximum value set;

[0072] Decompose the coordinates (x, y) into the integer part x0, y0 and the decimal part dx, dy, and the following relationship holds: x=x0+dx y=y0+dy

[0073] Then take 16 adjacent pixels centered at (x0, y0) and mark them as I(x i ,y i ), where i, j = 0, 1, 2, 3;

[0074] Then reconstruct the similarity matrix according to Formula 6, which is:

[0075] in,

[0076] In one embodiment, the maximum value corresponding to each reconstructed matrix is ​​the maximum value of the position index of the reconstructed matrix, which can be represented by (x, y). Since each reconstructed matrix has a maximum value, a maximum value set is obtained, and the number of maximum values ​​in the maximum value set is the same as the number of reconstructed matrices and the number of similar matrices.

[0077] By reconstructing the similarity matrix, videos with disturbance effects can be screened, making the subsequent camera disturbance effect evaluation and elimination more effective.

[0078] S1f: Obtain the number of frames of the framed frame set, determine a vibration time history signal according to the number of frames of the framed frame set and the maximum value set, and use the vibration time history signal as the signal to be processed.

[0079] The vibration time history signal is obtained by formula 7, which is:

[0080] Where n is the number of image frames, H i and L i They are respectively the width position and height position of the maximum value of the image mapping value of the i-th frame.

[0081] By calculating the vibration time history signal, it is convenient to perform vibration analysis on the photographed object, and then eliminate the disturbance effect of the camera, thereby improving the efficiency and effectiveness of camera disturbance effect evaluation and elimination.

[0082] Please refer to FIG. 4 , which is a schematic diagram of another sub-flow in the second embodiment of the camera disturbance effect evaluation and elimination method of the present application.

[0083] Based on the above first embodiment, in this embodiment, step S1 includes:

[0084] S11: Initializing the signal to be processed to obtain an initialization signal;

[0085] S12: Eliminate the DC high-frequency signal in the initialization signal to obtain a preliminary signal;

[0086] S13: performing signal decomposition on the preliminary signal according to a preset number of layers to obtain the first signal set;

[0087] Initialization processing refers to decomposing the signal to be processed into multiple modal signals and multiple modal frequencies. It should be noted that the specific means of initialization processing can be Fourier transform or wavelet transform of the signal to be processed, which is not specifically limited here.

[0088] The initial signal is obtained by removing abnormal signals (DC high-frequency signals) from the obtained multi-order modal signals and multi-order modal frequencies, thereby improving the accuracy of the obtained signal. The preset number of layers is a manually set number of layers, which can be set according to different displacement time-history signals. Decomposing the preliminary signal according to the preset number of layers improves the accuracy of determining various signal characteristics, facilitating the subsequent elimination of camera disturbance effects.

[0089] Please refer to FIG. 5 , which is a schematic diagram of another sub-flow in the second embodiment of the camera disturbance effect evaluation and elimination method of the present application.

[0090] Based on the above first embodiment, in this embodiment, step S4 includes:

[0091] S41: Acquire an acceleration signal collected by an accelerometer, where the accelerometer is disposed on an object photographed by a camera;

[0092] S42: performing frequency domain conversion on the acceleration signal to obtain a reference signal;

[0093] When a camera is shooting a video, an accelerometer is attached to the object being filmed. The data collected by the accelerometer is analyzed to obtain an acceleration time history signal (acceleration signal). It should be noted that the accelerometer only starts collecting data when the video starts shooting, and the two are synchronized.

[0094] The acceleration signal is converted into frequency domain using Formula 8, which is:

[0095] Where a(t) is the acceleration signal in the tth time domain, i is the frequency domain signal corresponding to t, and N is the time domain length of the signal.

[0096] The signal obtained after frequency domain conversion of the acceleration signal is used as a reference signal for subsequent comparison of the disturbance effect and the disturbance elimination effect, thereby improving the efficiency of camera disturbance effect evaluation and elimination.

[0097] S43: obtaining a plurality of frequency domain mirror signals based on the curve information in the curve information set;

[0098] S44: Calculating first-order derivatives and second-order derivatives of the frequency domain mirror signal and the reference signal respectively to obtain a derivative calculation result set;

[0099] S45: Obtaining a frequency domain mirror index set according to the derivative calculation result set and the mirror index formula;

[0100] The frequency domain mirror signal is obtained based on the curve information (curve amplitude, curve shape, and curvature radius) in the curve information set. The first-order derivative and second-order derivative of the frequency domain mirror signal and the reference signal are then calculated. The first-order derivative and second-order derivative of the frequency domain mirror signal are respectively calculated as f'(i) and f″(i), and the first-order derivative and second-order derivative of the reference signal are respectively calculated as: F'(i) and F″(i). The frequency domain mirror index is then calculated according to Formula 9 (Mirror Index Formula), which is:

[0101] Among them, · represents vector dot multiplication operation, || represents absolute value operation, × represents multiplication numerical operation; σ f and σ F represent the standard deviation of the frequency domain mirror signal f(i) and the standard deviation of the reference signal F(i) respectively; and Represent the average values ​​of f(i) and F(i) respectively. k Indicates the frequency domain image index for removing the k-th order modal signal.

[0102] The frequency domain mirror index can be used to quantitatively evaluate the disturbance effect, avoiding human subjective judgment, reducing errors, and thus improving the effectiveness of camera disturbance effect evaluation and elimination.

[0103] Please refer to FIG. 6 , which is a schematic diagram of another sub-flow in the second embodiment of the camera disturbance effect evaluation and elimination method of the present application.

[0104] Based on the above first embodiment, in this embodiment, step S5 includes:

[0105] S51: performing comparative calculation on the multiple frequency domain mirror indicators in the frequency domain mirror indicator set to obtain a maximum value in the frequency domain mirror indicator set, and taking the maximum value in the frequency domain mirror indicator set as a maximum frequency domain mirror indicator;

[0106] S52: determining, based on the maximum frequency domain image indicator, a signal corresponding to the maximum frequency domain image indicator to be removed from the first signal set, and using the signal corresponding to the maximum frequency domain image indicator to be removed from the first signal set as a removal signal;

[0107] S53: determining a disturbance signal in the signal to be processed according to the rejection signal and preset order data;

[0108] S54: Eliminate the disturbance signal from the signal to be processed to obtain a disturbance cancellation signal.

[0109] Compare the various frequency domain mirror indicators in the frequency domain mirror indicator set to obtain the frequency domain mirror indicator with the largest value, and use the frequency domain mirror indicator with the largest value as the maximum frequency domain mirror indicator, indicating that the corresponding signal position has the largest disturbance effect. In the above embodiment, it is known that each frequency domain mirror indicator corresponds to the signal removed from the first indicator set, and the maximum frequency domain mirror indicator corresponds to the signal removed from the first indicator set. It should be noted that if the disturbance effect is to be eliminated, the point with the largest disturbance effect must be eliminated, so the signal eliminated from the first indicator set is used as the elimination signal, and the elimination range is determined according to the preset order data. Simply put, the signal eliminated from the first indicator set is used as an endpoint, and then the other endpoint is determined according to the preset order data. The obtained signal segment is used as the disturbance signal, and the disturbance signal segment is eliminated from the signal to be processed, thereby obtaining the disturbance elimination signal. The step of eliminating the disturbance signal can be expressed by formula 10: x′(t)≈x(t)-∑u s (t)e iωst

[0110] in, is the disturbance elimination signal, x(t) is the signal to be processed, ∑u s (t)e iωst is the disturbance signal.

[0111] The location where the disturbance effect is most severe is determined by the maximum frequency domain mirror index, and then the elimination range is determined based on the continuity of the signal and the preset order data, so as to obtain the disturbance elimination signal after the disturbance is eliminated, thereby obtaining an accurate vibration time history signal, thereby eliminating and reducing the influence of the camera disturbance effect on the vibration measurement.

[0112] Please refer to FIG. 7 , which is a schematic diagram of a sub-flow in the third embodiment of the camera disturbance effect evaluation and elimination method of the present application.

[0113] Based on the above embodiments, in this embodiment, before step S3, the following steps are further included:

[0114] S3a: Obtaining eigenfunctions corresponding to the plurality of second signal sets;

[0115] S3b: determining corresponding preferred values ​​of the eigenfunction and the preset parameters according to the eigenfunction and the preset parameters; and

[0116] S3c: If the preferred value of the preset parameter meets the preset iteration stopping criterion, reconstruct the corresponding preferred value of the eigenfunction to obtain the optimized reconstructed spectrum set corresponding to the multiple second signal sets.

[0117] Step S3b includes:

[0118] S3b1: dividing the preset parameters into eigenfrequency and regularization parameters;

[0119] S3b2: Construct an optimization formula based on the eigenfunction, the eigenfrequency and the regularization parameter; the optimization formula is:

[0120] Among them, u k (t) is the Kth eigenfunction, ω k is the Kth eigenfrequency, α k (t) is the Kth regularization parameter, t is time, K is the number of eigenfunctions, and x(t) is the signal to be processed.

[0121] S3b3: Fix the eigenfunction and the regularization parameter, and calculate the partial derivative of the eigenfrequency according to the optimization formula to obtain the optimal value of the eigenfrequency;

[0122] S3b4: fixing the regularization parameter and the eigenfrequency, taking partial derivatives of the eigenfunction according to the optimization formula to obtain an optimal value of the eigenfunction, and updating the optimal value of the eigenfunction into the optimization formula;

[0123] S3b5: Fixing the eigenfunction and the eigenfrequency, and taking partial derivatives of the regularization parameter according to the optimization formula to obtain an optimal value of the regularization parameter;

[0124] Regularization parameters include regularization parameters and Lagrange multiplier parameters. Set regularization parameters α1, α2, ..., α k , the number of modes K, the Lagrange multiplier parameter λ 1， λ2,...,λ k , camera frame rate fps and preset iteration standard Δε, k eigenfunctions are μ1(t), ..., μ k (t), k eigenfrequencies are ω1,…,ω k . Create the optimization formula based on the above parameters:

[0125] Among them, u k (t) is the Kth eigenfunction, ω k is the Kth eigenfrequency, α k (t) is the Kth regularization parameter, t is time, K is the number of eigenfunctions, and x(t) is the signal to be processed.

[0126] Then iterate the calculation according to the following sequence:

[0127] (1) Fixed u k (t) and α k , the optimization formula is applied to ω k Perform partial derivative to obtain the optimal ωk .

[0128] (2) Fixed α k and ω k , optimize the formula for u k (t) Perform partial derivative to obtain the optimal u k (t), and u k (t)To update.

[0129] (3) Fixed u k (t) and ω k , the optimization formula for α k Perform partial derivative to obtain the optimal α k , and for α k to update.

[0130] (4) Fixed u k (t) and α k , the optimization formula is applied to ω k Take partial derivative and calculate ω k to update.

[0131] (5) Check whether the following preset iteration stopping criteria are met:

[0132] If it is satisfied, stop the iteration, otherwise continue to iterate and calculate the above steps (2)(3)(4), and output the optimal μ1(t), ..., μ k (t).

[0133] Formula 11 is used to reconstruct the corresponding eigenfunction optimal value to obtain the optimized reconstructed spectrum sets corresponding to the multiple second signal sets. Formula 11 is:

[0134] On the basis of the above embodiment, before performing frequency domain analysis on multiple second signal sets to obtain the curve information set, the second signal set is made more accurate by determining the optimal values ​​of the signal eigenfunction and preset parameters in the first signal set, thereby improving the subsequent camera disturbance effect evaluation and elimination effect. According to the optimization formula and the method of calculating partial derivatives, the efficiency of camera disturbance effect evaluation and elimination is improved, and processing time is saved.

[0135] In one embodiment, step 1: setting parameter change restriction conditions, introspection parameters c, recombination period T, population size m, number of matrix rows M, setting iteration termination conditions, including: setting the maximum number of iterations genmax, setting iteration loop termination conditions: including the maximum number of modes Kmax, and preset order data s.

[0136] Step 2: Start the loop from K=2 and initialize the array A with a fixed number of rows of M and a number of columns of 2K+2. The array A includes: columns 1 to K represent the randomly generated regularization parameters α1, α2, ..., α k , columns K+1 to 2K represent the randomly generated Lagrange multiplier parameters λ1, λ2, ..., λ k , the 2K+1 column represents the generated positive integer preset order data, and the 2K+2 column represents the frequency domain mirror index K k .

[0137] Step 3: For each row in the A matrix, based on the first 2K+1 column parameters, calculate the frequency domain image index according to the method provided in the above embodiment, and output the maximum frequency domain image index as the value of the 2K+2 column of the corresponding row.

[0138] Step 4: In the matrix A, every m rows are regarded as a group of population data, and in each population group, the 2K+2 column index K is selected. k The row vector corresponding to the maximum is used as the optimal parameter Gibest(j) of the matrix; i represents the sequence number of the population, i=1, 2,…, (M / m); j represents the 2K+1 parameters in the matrix A.

[0139] Step 5: Update the parameters of matrix A based on the optimal parameters obtained in the above steps and the set introspection parameter c to generate the updated matrix A′, and calculate the K of each row and column 2K+2 based on the above steps. k Indicator value.

[0140] Step 6: Compare the K of each row in matrix A and matrix A′ k Indicator, select K k The rows of data with large indexes form a matrix A", and the index K is selected from each group of data in the matrix A k The row vector corresponding to the maximum is taken as the optimal parameter Gibest(j)′ of the group, and the global optimal parameter Gbest is selected from the optimal parameter column.

[0141] Step 7: Randomly update the matrix A″ based on the optimal parameter column to obtain the matrix A″′. First, determine whether the iteration termination condition is met, and then determine whether the loop termination condition is met: If the iteration termination condition is met: the number of iterations ≥ genmax, then output a row of parameter vectors corresponding to the maximum value of the current Kk index, otherwise execute step 8; If the iteration termination condition is met, further determine whether the loop termination condition is met. If the loop termination condition is met: K>Kmax, terminate the calculation, otherwise set K=K+1.

[0142] Step 8: When the number of iterations is an integer multiple of the set recombination period T, the matrix A″′ is randomly disrupted, and steps 3 to 7 are executed again.

[0143] In one embodiment, during the parameter updating process in steps 1 to 5, the optimization parameter restriction condition is satisfied.

[0144] In one embodiment, the formula for updating the 2K+1 column parameters of matrix A in step 5 is: ij =c*A ij +r*(Gi best (j)-A ij );

[0145] Where, i = 1, 2, ..., (M / m); j = 1, 2, ..., 2K+1, A ij and A′ ij denote the values ​​of the parameters at row i and column j in matrix A and matrix A′, respectively, and r denotes a random number in the range of 0 to 1;

[0146] In one embodiment, in step 7, A″ is randomly updated based on the optimal parameter column to obtain a matrix A″′;

[0147] The formula for updating the 2K+1 column parameters of the matrix A″ is: ij =A″ ij +r1×(Gr best -A″ ij +r2×(G best -A″ ij )+r3×(A″ kj -A″ ij );

[0148] A″ ij and A″′ ij Represent the values ​​of the parameters in the i-th row and j-th column of the matrix A″ and the matrix A″′ respectively; i = 1, 2, …, (M / m); j = 1, 2, …, 2K+1, r1, r2, r3∈rand(0,1);

[0149] In one embodiment, after step 8, the method further includes:

[0150] Step 9: According to the first 2K columns of the output optimal parameter vector, based on the method provided in the above embodiment of this application, the first s-order mode U(t) with the largest frequency domain mirror index is obtained, and it is removed from the original structural vibration time history signal (signal to be processed).

[0151] On the basis of the above embodiment, through the above preferred steps, when calculating the frequency domain mirror index, the time for obtaining the optimal parameter value is saved, the efficiency of obtaining the optimal parameter value is improved, and the efficiency of evaluating and eliminating the camera disturbance effect is thereby improved.

[0152] In addition, an embodiment of the present application further proposes a storage medium, which stores a camera disturbance effect evaluation and elimination program. When the camera disturbance effect evaluation and elimination program is executed by a processor, the steps of the camera disturbance effect evaluation and elimination method described above are implemented.

[0153] Refer to FIG8 , which is a structural block diagram of a first embodiment of a device for evaluating and eliminating camera disturbance effects of the present application.

[0154] The camera disturbance effect evaluation and elimination device 700 includes:

[0155] A signal decomposition module 701 is configured to decompose a signal to be processed to obtain a first signal set, wherein the signal to be processed is a signal obtained by analyzing a video captured by a camera;

[0156] A elimination generation module 702 is configured to select a different signal from the first signal set for elimination each time, to obtain multiple second signal sets;

[0157] A frequency domain analysis module 703 is configured to perform frequency domain analysis on the plurality of second signal sets to obtain a curve information set;

[0158] a frequency domain mirror module 704, configured to determine a frequency domain mirror indicator set based on the curve information in the curve information set and a mirror indicator formula, wherein the frequency domain mirror indicator set includes a plurality of frequency domain mirror indicators, and each frequency domain mirror indicator corresponds to a signal removed from the first signal set; and

[0159] The disturbance elimination module 705 is configured to determine a maximum frequency domain image index from the multiple frequency domain image indexes, determine a disturbance signal in the signal to be processed according to the maximum frequency domain image index, and eliminate the disturbance signal to obtain a disturbance elimination signal.

[0160] Among them, the signal decomposition module 701 includes: an initial unit, used to initialize the signal to be processed to obtain an initialization signal; a removal unit, used to remove the DC high-frequency signal in the initialization signal to obtain a preliminary signal; and a preset decomposition unit, which decomposes the preliminary signal according to a preset number of layers to obtain the first signal set.

[0161] The frequency domain mirror module 704 includes: an acceleration unit, used to obtain an acceleration signal collected by an accelerometer, wherein the accelerometer is set on the object photographed by the camera; a frequency domain conversion unit, used to perform frequency domain conversion on the acceleration signal to obtain a reference signal; a mirror acquisition unit, used to obtain multiple frequency domain mirror signals based on the curve information in the curve information set; a derivative calculation unit, used to respectively calculate the first-order derivative and second-order derivative of the frequency domain mirror signal and the reference signal to obtain a derivative calculation result set; and a mirror index unit, used to obtain a frequency domain mirror index set based on the derivative calculation result set and the mirror index formula.

[0162] The disturbance elimination module 705 includes: a maximum frequency domain unit, used to compare and calculate the multiple frequency domain mirror indicators in the frequency domain mirror indicator set, obtain the maximum value in the frequency domain mirror indicator set, and use the maximum value in the frequency domain mirror indicator set as the maximum frequency domain mirror indicator; a corresponding elimination unit, used to determine the signal corresponding to the maximum frequency domain mirror indicator to be eliminated from the first signal set according to the maximum frequency domain mirror indicator, and use the signal corresponding to the maximum frequency domain mirror indicator to be eliminated from the first signal set as the elimination signal; a disturbance signal unit, used to determine the disturbance signal in the signal to be processed according to the elimination signal and preset order data; and a disturbance elimination unit, used to eliminate the disturbance signal in the signal to be processed to obtain a disturbance elimination signal.

[0163] This embodiment generates multiple second signal sets by decomposing the signal to be processed and then eliminating them one by one. Then, multiple frequency domain mirror indicators are obtained based on the curve information and mirror indicator formula obtained after frequency domain analysis of the multiple second signal sets. The disturbance signal is then determined based on the maximum frequency domain mirror indicator, and the disturbance signal is eliminated to obtain a disturbance elimination signal, thereby improving the accuracy of vibration measurement and realizing the evaluation and elimination of camera disturbance effects. This application quantitatively evaluates the disturbance effect by determining the frequency domain mirror indicator, thereby determining the part of the vibration time history signal with severe disturbance effects, and eliminating this part to obtain an accurate vibration time history signal, thereby improving the accuracy of vibration measurement and realizing the evaluation and elimination of camera disturbance effects.

[0164] Based on the first embodiment of the camera disturbance effect evaluation and elimination device mentioned above in the present application, a second embodiment of the camera disturbance effect evaluation and elimination device of the present application is proposed.

[0165] In one embodiment, the camera disturbance effect evaluation and elimination device also includes a normalized signal submodule, which includes: a normalized video unit, which is used to normalize the video shot by the camera to obtain a normalized video; a framing unit, which is used to frame the normalized video to obtain a frame set; a moving unit, which is used to move the preset normalized template on each frame in the frame set according to a first preset rule, and determine the similarity matrix according to the mapping value of each movement position to obtain multiple similarity matrices; a reconstruction unit, which is used to reconstruct each similarity matrix in the multiple similarity matrices to obtain a reconstructed matrix set; a maximum value acquisition unit, which is used to obtain the maximum value corresponding to each reconstructed matrix in the reconstructed matrix set to obtain a maximum value set; and a target signal acquisition unit, which is used to obtain the number of frames in the frame set, determine the vibration time history signal according to the number of frames in the frame set and the maximum value set, and use the vibration time history signal as the signal to be processed.

[0166] In one embodiment, the camera disturbance effect evaluation and elimination device also includes a preferred reconstruction submodule, which includes: an eigenfunction unit for obtaining the eigenfunctions corresponding to the multiple second signal sets; a preferred determination unit for determining the corresponding eigenfunction preferred values ​​and the preferred values ​​of the preset parameters based on the eigenfunctions and the preset parameters; and a preferred reconstruction unit for reconstructing the corresponding eigenfunction preferred values ​​if the preferred values ​​of the preset parameters meet the preset iteration stopping criteria, so as to obtain the optimized reconstructed spectrum sets corresponding to the multiple second signal sets.

[0167] The preferred determination unit includes: a parameter classification subunit for dividing the preset parameters into eigenfrequency and regularization parameters; an optimization construction subunit for constructing an optimization formula based on the eigenfunction, the eigenfrequency and the regularization parameters: Among them, u k (t) is the Kth eigenfunction, ω k is the Kth eigenfrequency, α k (t) is the Kth regularization parameter, t is time, K is the number of eigenfunctions, and x(t) is the signal to be processed; a frequency optimization subunit is used to fix the eigenfunction and the regularization parameter, and to take the partial derivative of the eigenfrequency according to the optimization formula to obtain the optimal value of the eigenfrequency; a function optimization subunit is used to fix the regularization parameter and the eigenfrequency, and to take the partial derivative of the eigenfunction according to the optimization formula to obtain the optimal value of the eigenfunction, and to update the optimal value of the eigenfunction to the optimization formula; and a parameter optimization subunit is used to fix the eigenfunction and the eigenfrequency, and to take the partial derivative of the regularization parameter according to the optimization formula to obtain the optimal value of the regularization parameter.

[0168] This embodiment generates multiple second signal sets by decomposing the signal to be processed and then eliminating them one by one. Then, multiple frequency domain mirror indicators are obtained based on the curve information obtained after frequency domain analysis of the multiple second signal sets. The disturbance signal is determined based on the maximum frequency domain mirror indicator, and the disturbance signal is eliminated to obtain a disturbance elimination signal, thereby improving the accuracy of vibration measurement and realizing the evaluation and elimination of camera disturbance effects. This application quantitatively evaluates the disturbance effect by determining the frequency domain mirror indicator, thereby determining the part of the vibration time history signal with severe disturbance effects, and eliminating this part to obtain an accurate vibration time history signal, thereby improving the accuracy of vibration measurement and realizing the evaluation and elimination of camera disturbance effects.

[0169] Other embodiments or specific implementation methods of the camera disturbance effect evaluation and elimination device of the present application can refer to the above-mentioned method embodiments and will not be repeated here.

[0170] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0171] The above are merely optional embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for evaluating and eliminating camera perturbation effects, wherein, The method includes: Performing signal decomposition on the signal to be processed to obtain a first signal set, where the signal to be processed is a signal obtained by analyzing the video captured by the camera; Each time, selecting a different signal from the first signal set for elimination to obtain a plurality of second signal sets; Performing frequency-domain analysis on the plurality of second signal sets to obtain a curve information set; Determining a frequency-domain mirror image index set based on the curve information in the curve information set and a mirror image index formula, where the frequency-domain mirror image index set includes a plurality of frequency-domain mirror image indexes, and each frequency-domain mirror image index corresponds to the signal eliminated from the first signal set; and Determining the maximum frequency-domain mirror image index from the plurality of frequency-domain mirror image indexes, determining the disturbance signal in the signal to be processed according to the maximum frequency-domain mirror image index, and eliminating the disturbance signal to obtain a disturbance-eliminated signal.

2. The camera perturbation effect evaluation and elimination method according to claim 1, wherein, Before performing signal decomposition on the signal to be processed to obtain a first signal set, it further includes: Performing normalization processing on the video captured by the camera to obtain a normalized video; Framing the normalized video to obtain a framed frame set; Moving a preset normalization template on each frame in the framed frame set according to a first preset rule, and determining a similarity matrix according to the mapping value of each movement position to obtain a plurality of similarity matrices; Respectively reconstructing each similarity matrix in the plurality of similarity matrices to obtain a reconstructed matrix set; Obtaining the maximum value corresponding to each reconstructed matrix in the reconstructed matrix set to obtain a maximum value set; and Obtaining the number of frames of the framed frame set, determining a vibration time history signal according to the number of frames of the framed frame set and the maximum value set, and using the vibration time history signal as the signal to be processed.

3. The camera perturbation effect evaluation and elimination method according to claim 2, wherein, The normalization refers to the process of transforming a dimensional expression into a dimensionless expression and becoming a scalar through transformation.

4. The camera perturbation effect evaluation and elimination method according to claim 2, wherein, The first preset rule is to move from the vertex of each frame, close to the frame, and move clockwise or counterclockwise until the entire frame is traversed.

5. The camera perturbation effect evaluation and elimination method according to claim 2, wherein, The distance of each movement is one pixel, and the mapping value is the value of the position where it is located on the frame after each movement.

6. The camera perturbation effect evaluation and elimination method according to claim 1, wherein, Performing signal decomposition on the signal to be processed to obtain a first signal set includes: Performing initialization processing on the signal to be processed to obtain an initialized signal; Eliminating the DC high-frequency signal in the initialized signal to obtain a preliminary signal; and Performing signal decomposition on the preliminary signal according to a preset number of layers to obtain the first signal set.

7. The camera perturbation effect evaluation and elimination method according to claim 6, wherein, The initialization processing refers to decomposing the signal to be processed into multi-order modal signals and multi-order modal frequencies.

8. The camera perturbation effect evaluation and elimination method according to claim 1, wherein, Before performing frequency-domain analysis on the plurality of second signal sets to obtain a curve information set, it further includes: Obtaining the eigenfunctions corresponding to the plurality of second signal sets; Determining the preferred value of the corresponding eigenfunction and the preferred value of the preset parameter according to the eigenfunction and the preset parameter; and If the preferred value of the preset parameter meets the preset iteration stop criterion, reconstructing the corresponding preferred value of the eigenfunction to obtain an optimized reconstructed spectrum set corresponding to the plurality of second signal sets.

9. The camera perturbation effect evaluation and elimination method according to claim 1, wherein, Before performing signal decomposition on the signal to be processed to obtain a first signal set, it further includes: After obtaining the video captured by the camera, perform displacement time history analysis on the object under test in the captured video, and use the analysis result as the signal to be processed.

10. The camera perturbation effect evaluation and elimination method according to claim 9, wherein, Before decomposing the signal to be processed to obtain the first signal set, it further includes: Construct an abnormal time history signal discrimination model trained by a large number of vibration time history signals, then use the abnormal time history signal discrimination model to discriminate the signal to be processed, and eliminate the abnormality of the signal to be processed according to the processing result.

11. The camera perturbation effect evaluation and elimination method according to claim 1, wherein, Perform frequency-domain analysis on the multiple second signal sets through the following formula to obtain a curve information set: Among them, x(t) is the vibration time history signal of the t-th time domain discrete point, i is the frequency domain discrete point signal corresponding to t, N is the time domain length of the signal; f(k) is the frequency domain vector of the structural vibration time history signal.

12. The method for evaluating and eliminating camera perturbation effects according to claim 1, wherein, The index formula in the frequency-domain mirror image index set determined based on the curve information in the curve information set and the mirror image index formula is as follows: where, · represents the vector dot product operation, || represents the absolute value operation, and × represents the multiplication numerical operation; σ f and σ F represent the standard deviation of f(i) and the standard deviation of F(i), respectively; And Respectively represent the average values of f(i) and F(i). Kk represents the frequency domain mirror image index for removing the k-th order modal signal.

13. The camera perturbation effect evaluation and elimination method according to claim 12, wherein, The curve information includes the curve amplitude, curve shape and curve force radius of the curve.

14. The camera perturbation effect evaluation and elimination method according to claim 8, wherein, The determining the corresponding optimal value of the eigenfunction and the optimal value of the preset parameter according to the eigenfunction and the preset parameter includes: Dividing the preset parameter into an eigenfrequency and a regularization parameter; Construct an optimization formula based on the eigenfunction, the eigenfrequency, and the regularization parameter: where u k (t) is the k-th eigenfunction, ω k is the k-th eigenfrequency, α k is the k-th regularization parameter, t is time, K is the number of eigenfunctions, and x(t) is the signal to be processed; Fixing the eigenfunction and the regularization parameter, taking the partial derivative of the eigenfrequency according to the optimization formula to obtain the optimal value of the eigenfrequency; Fixing the regularization parameter and the eigenfrequency, taking the partial derivative of the eigenfunction according to the optimization formula to obtain the optimal value of the eigenfunction, and updating the optimal value of the eigenfunction to the optimization formula; and Fixing the eigenfunction and the eigenfrequency, taking the partial derivative of the regularization parameter according to the optimization formula to obtain the optimal value of the regularization parameter.

15. The method for evaluating and eliminating camera perturbation effects according to any one of claims 1 to 14, wherein, The determining the frequency domain mirror image index set based on the curve information in the curve information set and the mirror image index formula includes: Obtain the acceleration signal collected by the accelerometer, and the accelerometer is arranged on the object captured by the camera; Perform frequency domain conversion on the acceleration signal to obtain a reference signal; Based on the curve information in the curve information set, obtain a plurality of frequency domain mirror image signals; Calculate the first derivative and the second derivative of the frequency domain mirror image signal and the reference signal respectively to obtain a derivative calculation result set; and According to the derivative calculation result set and the mirror image index formula, obtain the frequency domain mirror image index set.

16. The camera perturbation effect evaluation and elimination method according to any one of claims 1 to 14, wherein, The determining the maximum frequency domain mirror image index from the plurality of frequency domain mirror image indexes, determining the disturbance signal in the signal to be processed according to the maximum frequency domain mirror image index, and removing the disturbance signal to obtain a disturbance elimination signal includes: Perform a comparison calculation on the plurality of frequency domain mirror image indexes in the frequency domain mirror image index set to obtain the maximum value in the frequency domain mirror image index set, and use the maximum value in the frequency domain mirror image index set as the maximum frequency domain mirror image index; According to the maximum frequency domain mirror image index, determine the signal corresponding to be removed from the first signal set by the maximum frequency domain mirror image index, and use the signal corresponding to be removed from the first signal set by the maximum frequency domain mirror image index as the removal signal; According to the removal signal and the preset order data, determine the disturbance signal in the signal to be processed; and Remove the disturbance signal from the signal to be processed to obtain a disturbance elimination signal.

17. The method for evaluating and eliminating camera perturbation effects according to claim 16, wherein, The preset order data does not exceed the order of the signal to be processed.

18. The method for evaluating and eliminating the camera perturbation effect according to claim 16, wherein, Remove the disturbance signal from the signal to be processed according to the following formula to obtain a disturbance cancellation signal: x'(t)≈x(t)-∑u s (t)e iωst , Among them, x'(t) is the obtained disturbance cancellation signal, x (t) is the signal to be processed, u s (t) is the signal corresponding to the maximum frequency domain mirror image index, s is the preset order data, and t is time.

19. A camera perturbation effect evaluation and elimination device, wherein, The device includes a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the method for evaluating and eliminating the camera perturbation effect according to any one of claims 1 to 18 is implemented.

20. A computer-readable storage medium, wherein, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the method for evaluating and eliminating the camera perturbation effect according to any one of claims 1 to 18 is implemented.

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