A monitoring management method and system based on three-dimensional panoramic video fusion

Abstract

The application discloses a kind of based on three-dimensional panoramic video fusion monitoring management method and system, it is related to monitoring management field, wherein the method includes: obtaining first data acquisition result;Video decoding is carried out to it, obtains video frame image set;Obtain first feature point set;Texture feature analysis is carried out, obtains first texture feature set;According to first target view angle and the camera obtains first rectification transformation parameter;According to the first rectification transformation parameter based on the first feature point set and the first texture feature set carries out the fusion transformation of the video frame image set, obtains first fusion result, and according to it carries out the monitoring management of target position.Solve the technical problem that the accuracy of prior art is not high for monitoring management, and then cause the technical problem that monitoring management effect is poor.Improved the accuracy of monitoring management and accuracy, and then promote the effect and quality of monitoring management and so on technical effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of monitoring management, in particular, to a monitoring management method and system based on three-dimensional panoramic video fusion. BACKGROUND

[0002] With the rapid development of science and technology, video monitoring as an extension of human vision is widely used in various industries. In addition, video monitoring is an important means of security and visual management, and plays an irreplaceable role in modern security. However, with the expansion of the application scale of video monitoring, the existing monitoring management method is difficult to well express the spatial position relationship between the monitoring videos, thereby causing the discontinuity of the monitoring area and the monitoring target in time and space, and it is difficult for the monitoring personnel to quickly obtain the overall security situation of the monitoring area through discrete isolated videos.

[0003] In the prior art, there is a technical problem that the accuracy of monitoring management is not high, thereby causing poor monitoring management effect. SUMMARY

[0004] The present application provides a monitoring management method and system based on three-dimensional panoramic video fusion, which solves the technical problem that the accuracy of monitoring management is not high in the prior art, thereby causing poor monitoring management effect.

[0005] In view of the above problems, the present application provides a monitoring management method and system based on three-dimensional panoramic video fusion.

[0006] In the first aspect, the present application provides a monitoring management method based on three-dimensional panoramic video fusion, wherein the method is applied to a monitoring management system based on three-dimensional panoramic video fusion, and the method comprises: collecting target position video stream data through the camera to obtain a first data collection result; decoding the video according to the first data collection result to obtain a video frame image set, wherein each image in the video frame image set has time and collection position identification; performing coincident image calibration according to the collection angle of the camera and the video frame image set, selecting feature points according to the first coincident image calibration result to obtain a first feature point set; performing texture feature analysis on the same time frame images in the video frame image set to obtain a first texture feature set; obtaining first rectification transformation parameters according to a first target view angle and the camera; performing fusion transformation of the video frame image set based on the first feature point set and the first texture feature set according to the first rectification transformation parameters to obtain a first fusion result; and performing monitoring management of the target position according to the first fusion result.

[0007] In a second aspect, the application further provides a monitoring management system based on three-dimensional panoramic video fusion, wherein the system comprises: a data acquisition module, configured to acquire target position video stream data through a camera to obtain a first data acquisition result; a video decoding module, configured to decode video according to the first data acquisition result to obtain a video frame image set, wherein each image in the video frame image set has a time and an acquisition position identifier; a coincident image calibration module, configured to calibrate coincident images according to the acquisition angle of the camera and the video frame image set, select feature points according to a first coincident image calibration result, and obtain a first feature point set; a texture feature analysis module, configured to analyze texture features of images of the same time frame in the video frame image set to obtain a first texture feature set; a rectification transformation module, configured to obtain first rectification transformation parameters according to a first target view angle and the camera; a fusion transformation module, configured to perform fusion transformation of the video frame image set based on the first feature point set and the first texture feature set according to the first rectification transformation parameters to obtain a first fusion result; and a monitoring management module, configured to perform monitoring management of the target position according to the first fusion result.

[0008] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0009] The target position video stream data is acquired through the camera to obtain a first data acquisition result; the first data acquisition result is decoded to obtain a video frame image set; based on this, coincident image calibration is performed in combination with the acquisition angle of the camera to obtain a first coincident image calibration result, and then feature points are selected to obtain a first feature point set; texture features of images of the same time frame in the video frame image set are analyzed to obtain a first texture feature set; first rectification transformation parameters are obtained according to a first target view angle and the camera; fusion transformation of the video frame image set is performed based on the first feature point set and the first texture feature set to obtain a first fusion result; and monitoring management of the target position is performed according to the first fusion result. The accuracy and precision of monitoring management are improved, and the effect and quality of monitoring management are improved; meanwhile, three-dimensional panoramic video fusion is combined with monitoring management to design a method for optimizing monitoring management, and the intelligent and scientific process of monitoring management is promoted. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 FIG. 1 is a flowchart of a monitoring management method based on three-dimensional panoramic video fusion according to the application;

[0011] Figure 2A flowchart of a process of monitoring and managing a target position according to a second fusion result in a monitoring and management method based on three-dimensional panoramic video fusion of the present application;

[0012] Figure 3 A flowchart of a process of monitoring and managing a target position according to a fifth fusion result in a monitoring and management method based on three-dimensional panoramic video fusion of the present application;

[0013] Figure 4 A structural diagram of a monitoring and management system based on three-dimensional panoramic video fusion of the present application.

[0014] Legend: data acquisition module 11, video decoding module 12, coincident image calibration module 13, texture feature analysis module 14, rectification transformation module 15, fusion transformation module 16, monitoring and management module 17. DETAILED DESCRIPTION

[0015] The present application provides a monitoring and management method and system based on three-dimensional panoramic video fusion, which solves the technical problem of low accuracy of monitoring and management in the prior art, thereby causing poor monitoring and management effect. The present application improves the accuracy and accuracy of monitoring and management, thereby improving the effect and quality of monitoring and management. Meanwhile, the present application combines three-dimensional panoramic video fusion with monitoring and management, designs a method for optimizing monitoring and management, and promotes the intelligent and scientific process of monitoring and management.

[0016] Embodiment one

[0017] Please refer to the accompanying Figure 1 The present application provides a monitoring and management method based on three-dimensional panoramic video fusion, wherein the method is applied to a monitoring and management system based on three-dimensional panoramic video fusion, and the method specifically comprises the following steps:

[0018] Step S100: acquiring target position video stream data by the camera to obtain a first data acquisition result;

[0019] Step S200: decoding the video according to the first data acquisition result to obtain a video frame image set, wherein each image in the video frame image set has time and acquisition position identification;

[0020] Specifically, a target position video stream data is collected by using a camera to obtain a first data collection result, and video decoding is performed on the first data collection result to obtain a video frame image set. The target position is any position for intelligent monitoring management using the monitoring management system based on three-dimensional panoramic video fusion. The target position may be a school, a hospital, a residential area, or the like. The first data collection result includes video data information collected by the camera within a certain time range. The video decoding refers to the process of intercepting image data information corresponding to the first data collection result. The video frame image set includes image data information corresponding to each frame of video in the first data collection result. Each image in the video frame image set has a time and a collection position identifier. The first data collection result is obtained, and video decoding is performed to determine the video frame image set, thereby providing data support for the subsequent intelligent monitoring management process.

[0021] Step S300: Coincidence image calibration is performed according to the collection angle of the camera and the video frame image set, feature point selection is performed according to the first coincidence image calibration result, and a first feature point set is obtained.

[0022] Further, step S300 of the present application further includes:

[0023] Step S310: A first feature evaluation model is constructed, wherein the first feature evaluation model is constructed by using feature data as input information and identification data as identification information.

[0024] Step S320: The first coincidence image calibration result is input into the first feature evaluation model to obtain a first output result.

[0025] Step S330: The first feature point set is obtained according to the first output result.

[0026] Specifically, the video frame image set is calibrated using the camera capture angle when collecting the target position video stream data to obtain a first coincident image calibration result, which is input into the first feature evaluation model as input information, and a first output result is output, and the first feature point set is obtained according to the first output result. The first coincident image calibration result is used to represent the image data information in the video frame image set that coincides with the capture angle of the camera. The first feature evaluation model has the functions of intelligent analysis and scientific evaluation of the input first coincident image calibration result. The first output result includes adaptability, rationality, accuracy and other parameter information of the first coincident image calibration result. After comprehensive analysis of the first output result by the monitoring management system based on three-dimensional panoramic video fusion, the first feature point set is determined. The first feature point set includes a plurality of feature points in the first coincident image calibration result. The first feature point set with high reliability is obtained by using the first coincident image calibration result and the first feature evaluation model, which provides data support for subsequent acquisition of the first fusion result.

[0027] Step S400: texture feature analysis is performed on the same time frame images in the video frame image set to obtain a first texture feature set;

[0028] Step S500: obtaining a first rectification transformation parameter according to the first target view angle and the camera;

[0029] Specifically, texture feature is a very important feature in an image, which is caused by the physical properties of the surface of an object, and different physical surfaces will produce different texture features. The texture feature analysis refers to a process of extracting the texture features of the same time frame images in the video frame image set by a certain image processing technology. The first texture feature set includes texture feature data information of coincident images and non-coincident images of the same time frame images in the video frame image set. For example, the first texture feature set includes parameters such as fineness, contrast, linearity, roughness, regularity and the like of the same time frame images in the video frame image set. Further, the first rectification transformation parameter is determined by using the first target view angle and the camera. The first target view angle is any view angle of the target position. The first target view angle can be automatically determined by the monitoring management system based on three-dimensional panoramic video fusion, or can be adaptively set according to actual conditions. The first rectification transformation parameter is data information used to represent the positional difference and directional difference between the first target view angle and the camera. The first texture feature set and the first rectification transformation parameter with high accuracy are obtained, which lays a foundation for subsequent fusion transformation of the video frame image set.

[0030] Step S600: performing fusion transformation on the video frame image set based on the first feature point set and the first texture feature set according to the first rectification transformation parameter, to obtain a first fusion result;

[0031] Step S700: performing monitoring management on the target position according to the first fusion result.

[0032] Specifically, based on the first feature point set and the first texture feature set, the video frame image set is fused and transformed by the first rectification transformation parameter to obtain a first fusion result, and the monitoring management on the target position is performed according to the first fusion result. Wherein, the first fusion result is image data information obtained by projecting the video frame image set to a first target view angle using the first rectification transformation parameter. The technical effect of obtaining a first fusion result with high credibility and performing monitoring management on the target position according to the first fusion result is achieved, thereby improving the accuracy of monitoring management and the effect and quality of monitoring management.

[0033] Further, the step S600 of the present application further comprises:

[0034] Step S610: performing imaging evaluation on the video frame image set to obtain a first imaging evaluation result;

[0035] Step S620: determining whether there is an image that does not satisfy a first preset imaging evaluation threshold in the first imaging evaluation result;

[0036] Step S630: when there is an image that does not satisfy the first preset imaging evaluation threshold in the first imaging evaluation result, obtaining a first transformation angle image;

[0037] Step S640: performing image fusion transformation according to the first transformation angle image.

[0038] Specifically, the video frame image set is automatically evaluated by the monitoring management system based on three-dimensional panoramic video fusion to obtain a first imaging evaluation result. Then, it is determined whether the first imaging evaluation result meets a first preset imaging evaluation threshold. If there is an image in the first imaging evaluation result that does not meet the first preset imaging evaluation threshold, a first transformed angle image is obtained and image fusion transformation is performed according to the first transformed angle image. The first imaging evaluation result is data information representing image quality parameters such as completeness, definition, fidelity, resolution, and the like of image data information in the video frame image set. The first preset imaging evaluation threshold is determined by the monitoring management system based on three-dimensional panoramic video fusion, which intelligently analyzes the key points and difficulties of monitoring management and image fusion transformation. The first transformed angle image is obtained by collecting images of the transformed angle of the image in the first imaging evaluation result that does not meet the first preset imaging evaluation threshold. For example, there is an image blur at a certain position in the video frame image set, which causes the image to not meet the first preset imaging evaluation threshold. Therefore, a new image is collected at the position by transforming the angle of the camera, and the clear image data information obtained at the position is the first transformed angle image. This achieves the technical effect of evaluating the video frame image set, obtaining a first transformed angle image with high accuracy according to the imaging evaluation result, preventing the image in the video frame image set that does not meet the first preset imaging evaluation threshold from affecting the accuracy of image fusion transformation, and improving the quality of image fusion transformation.

[0039] Further, as shown in FIG. 7, after step S700, the method further includes: Figure 2

[0040] Step S810: evaluating a pixel point view according to the first target time and the shooting angle of the camera to obtain a first view evaluation result;

[0041] Step S820: performing view pixel point elimination on the first fusion result according to the first view evaluation result, and obtaining a second fusion result according to the elimination result;

[0042] Step S830: performing monitoring management on the target position according to the second fusion result.

[0043] ​Specifically, on the basis of having obtained the first fusion result, pixel point view evaluation is performed on the first target time and the shooting angle of the camera to obtain a first view evaluation result; then, the first view evaluation result is used to obtain a view pixel point elimination result of the first fusion result, and a second fusion result is obtained according to the view pixel point elimination result, and then monitoring management of the target position is performed. The first target time refers to any time of performing monitoring management of the target position. The first target time can be adaptively set according to the actual monitoring management needs. The first view evaluation result includes pixel point quantity, pixel point position, and pixel point adaptation degree to monitoring management needs of the first fusion result. The elimination result includes the position and quantity of the pixel points with low adaptation degree to monitoring management needs in the first fusion result. The second fusion result is image data information obtained by eliminating the elimination result from the first fusion result. The technical effect of eliminating the view pixel points of the first fusion result and then obtaining a second fusion result with higher reliability and improving the accuracy of monitoring management of the target position is achieved.

[0044] Further, the step S820 of the application further includes:

[0045] Step S821: obtaining first light data, wherein the first light data includes light angle information and light intensity information;

[0046] Step S822: eliminating the light influence of the video frame image set according to the first light data and the shooting angle to obtain an elimination video frame image set;

[0047] Step S823: obtaining the second fusion result according to the elimination frame image set.

[0048] Specifically, according to the video frame image set, first light data is obtained, and the light influence of the video frame image set is eliminated in combination with the shooting angle to obtain an elimination video frame image set, and then the second fusion result is obtained. The first light data includes light angle information and light intensity information corresponding to image acquisition of the video frame image set. The elimination video frame image set includes image data information that affects the image quality of the video frame image set due to the influence of the first light data and the shooting angle. For example, there are defect images in the elimination video frame image set due to too high light intensity and poor shooting angle. The second fusion result includes image data information obtained by removing the elimination frame image set from the first fusion result. The technical effect of eliminating the light influence of the video frame image set, determining the elimination video frame image set, and obtaining a more accurate second fusion result according to the elimination video frame image set is achieved, and the accuracy of monitoring management is further improved.

[0049] Further, after step S830, the application further includes:

[0050] Step S840: performing light fitting of the second fusion result based on time series according to the first light data, and obtaining a third fusion result according to the fitting result;

[0051] Step S850: sending the third fusion result to the first user to obtain a first feedback evaluation result of the first user;

[0052] Step S860: performing linear mixing processing of the third fusion result according to the first feedback evaluation result to obtain a fourth fusion result;

[0053] Step S870: performing monitoring management of the target position according to the fourth fusion result.

[0054] Specifically, the obtained second fusion result is subjected to light fitting, a third fusion result is obtained according to the fitting result, and is sent to the first user to obtain a first feedback evaluation result of the first user; further, based on the first feedback evaluation result of the first user, a fourth fusion result is obtained, and the target position is monitored and managed according to the fourth fusion result. The fitting result is image data information for representing light fitting of the second fusion result based on time series using the first light data. The third fusion result is image data information of the second fusion result after adjustment according to the fitting result. The first user is any user who uses the monitoring management system based on three-dimensional panoramic video fusion for intelligent monitoring management. The first feedback evaluation result of the first user includes data information of the first user evaluating parameters such as accuracy and error degree of the third fusion result. The fourth fusion result is image data information of the third fusion result after linear mixing processing using the first feedback evaluation result. The fourth fusion result is used to perform monitoring management of the target position with high accuracy, and the technical effect of improving the quality of monitoring management is achieved.

[0055] Further, as shown in the accompanying Figure 3 After step S700, the application further includes:

[0056] Step S910: obtaining a first target feature;

[0057] Step S920: performing trajectory calibration on the first target feature to obtain a first trajectory calibration result;

[0058] Step S930: performing occlusion evaluation of the first fusion result according to the first trajectory calibration result to obtain first occlusion information;

[0059] Step S940: transparently processing the first occlusion information to obtain a fifth fusion result;

[0060] Step S950: monitoring and managing the target position according to the fifth fusion result.

[0061] Specifically, by trajectory calibration on the first target feature, a first trajectory calibration result is obtained, and occlusion evaluation of the first fusion result is performed according to the first trajectory calibration result to obtain first occlusion information; based on this, the first occlusion information is transparently processed to obtain a fifth fusion result; and the monitoring and management of the target position is performed according to the fifth fusion result. Wherein, the first target feature is any target feature in the monitoring and management of the target position, which can be adaptively set according to the actual monitoring and management needs. For example, the first target feature is a vehicle feature such as a license plate number or color of a vehicle. The first trajectory calibration result includes actual trajectory image data information of the first target feature. The first occlusion information is image data information of the first trajectory calibration result corresponding to the first fusion result that exists occlusion. The fifth fusion result is image data information after transparently processing and removing occlusion of the first occlusion information in the first fusion result. The technical effect of using the fifth fusion result to perform monitoring and management of the target position is achieved, thereby improving the accuracy of monitoring and management.

[0062] In summary, the monitoring and management method based on three-dimensional panoramic video fusion provided by the present application has the following technical effects:

[0063] By using a camera to collect target position video stream data, a first data collection result is obtained; video decoding is performed thereon to obtain a video frame image set; based on this, coincident image calibration is performed in combination with the collection angle of the camera to obtain a first coincident image calibration result, which is then subjected to feature point selection to obtain a first feature point set; texture feature analysis is performed on the same time frame images in the video frame image set to obtain a first texture feature set; first rectification transformation parameters are obtained according to a first target view angle and the camera; and fusion transformation of the video frame image set is performed based on the first feature point set and the first texture feature set to obtain a first fusion result; and monitoring and management of the target position is performed according to the first fusion result. The technical effects of improving the accuracy and accuracy of monitoring and management, and thereby improving the effect and quality of monitoring and management are achieved; at the same time, three-dimensional panoramic video fusion is combined with monitoring and management to design a method for optimizing monitoring and management, thereby promoting the intelligentization and scientization process of monitoring and management.

[0064] Embodiment Two

[0065] Based on the same inventive concept as the monitoring and management method based on three-dimensional panoramic video fusion in the foregoing embodiments, the present application also provides a monitoring and management system based on three-dimensional panoramic video fusion, please refer to the accompanyingFigure 4 The system comprises:

[0066] A data collection module 11, configured to collect target position video stream data through a camera, and obtain a first data collection result;

[0067] A video decoding module 12, configured to perform video decoding according to the first data collection result, and obtain a video frame image set, wherein each image in the video frame image set has time and collection position identification;

[0068] A coincident image calibration module 13, configured to perform coincident image calibration according to a collection angle of the camera and the video frame image set, perform feature point selection according to a first coincident image calibration result, and obtain a first feature point set;

[0069] A texture feature analysis module 14, configured to perform texture feature analysis on the same time frame images in the video frame image set, and obtain a first texture feature set;

[0070] A rectification transformation module 15, configured to obtain first rectification transformation parameters according to a first target visual angle and the camera;

[0071] A fusion transformation module 16, configured to perform fusion transformation of the video frame image set based on the first feature point set and the first texture feature set according to the first rectification transformation parameters, and obtain a first fusion result;

[0072] A monitoring management module 17, configured to perform monitoring management of the target position according to the first fusion result.

[0073] The application provides a monitoring management method based on three-dimensional panoramic video fusion, wherein the method is applied to a monitoring management system based on three-dimensional panoramic video fusion, and the method comprises the following steps: acquiring target position video stream data by using a camera to obtain a first data acquisition result; decoding the video to obtain a video frame image set; based on this, calibrating coincident images in combination with the camera's acquisition angle to obtain a first coincident image calibration result, then selecting feature points to obtain a first feature point set; analyzing texture features of the same time frame images in the video frame image set to obtain a first texture feature set; obtaining first rectification transformation parameters according to a first target view angle and the camera; performing fusion transformation on the video frame image set based on the first feature point set and the first texture feature set to obtain a first fusion result; and performing monitoring management on the target position according to the first fusion result. The technical problem that the accuracy of the monitoring management is not high in the prior art, and the monitoring management effect is poor is solved. The accuracy and accuracy of the monitoring management are improved, and the effect and quality of the monitoring management are improved. Meanwhile, the three-dimensional panoramic video fusion is combined with the monitoring management, a method for optimizing the monitoring management is designed, and the technical effect that the intelligentization and scientization process of the monitoring management are promoted is achieved.

[0074] The specification and drawings are only exemplary of the application, and the application is intended to include modifications and variations within the scope of the application and equivalents thereof.

Claims

1. A monitoring management method based on three-dimensional panoramic video fusion, characterized in that, The method is applied to a three-dimensional panoramic video fusion system in communication connection with a camera, and the method comprises: Collecting target position video stream data through the camera to obtain a first data collection result; Decoding the video according to the first data collection result to obtain a video frame image set, wherein each image in the video frame image set has time and collection position identification; Calibrating coincident images according to the collection angle of the camera and the video frame image set, selecting feature points according to the first coincident image calibration result to obtain a first feature point set; Analyzing texture features of the same time frame images in the video frame image set to obtain a first texture feature set; Obtaining first rectification transformation parameters according to a first target view angle and the camera; Performing fusion transformation of the video frame image set based on the first feature point set and the first texture feature set according to the first rectification transformation parameters to obtain a first fusion result; Performing monitoring management of the target position according to the first fusion result; Obtaining a first target feature; Calibrating a trajectory of the first target feature to obtain a first trajectory calibration result; Performing occlusion evaluation of the first fusion result according to the first trajectory calibration result to obtain first occlusion information; Performing transparent processing on the first occlusion information to obtain a fifth fusion result; Performing monitoring management of the target position according to the fifth fusion result.

2. The method of claim 1, wherein, The method further comprises: Performing pixel point view evaluation according to a first target time and the shooting angle of the camera to obtain a first view evaluation result; Performing view pixel point elimination of the first fusion result according to the first view evaluation result, and obtaining a second fusion result according to the elimination result; Performing monitoring management of the target position according to the second fusion result.

3. The method of claim 2, wherein, The method further comprises: Obtaining first light data, wherein the first light data comprises light angle information and light intensity information; Performing light influence elimination of the video frame image set according to the first light data and the shooting angle to obtain an elimination video frame image set; Obtaining the second fusion result according to the elimination frame image set.

4. The method of claim 3, wherein, The method further comprises: Performing time sequence-based light fitting of the second fusion result according to the first light data, and obtaining a third fusion result according to the fitting result; Sending the third fusion result to a first user to obtain a first feedback evaluation result of the first user; Performing linear mixing processing of the third fusion result according to the first feedback evaluation result to obtain a fourth fusion result; Performing monitoring management of the target position according to the fourth fusion result.

5. The method of claim 1, wherein, The method further comprises: Constructing a first feature evaluation model, wherein the first feature evaluation model is constructed by taking feature data as input information and taking identification data as identification information; Inputting the first coincident image calibration result into the first feature evaluation model to obtain a first output result; Obtaining the first feature point set according to the first output result.

6. The method of claim 1, wherein, The method further comprises: Perform imaging evaluation on the video frame image set to obtain a first imaging evaluation result; Determine whether there is an image that does not satisfy a first preset imaging evaluation threshold in the first imaging evaluation result; When there is an image that does not satisfy the first preset imaging evaluation threshold in the first imaging evaluation result, obtain a first transformation angle image; Perform image fusion transformation according to the first transformation angle image.

7. A monitoring management system based on three-dimensional panoramic video fusion, characterized by, The system is used to perform the method of any one of claims 1-6, and the system further comprises: A data acquisition module, configured to acquire target position video stream data through a camera to obtain a first data acquisition result; A video decoding module, configured to perform video decoding according to the first data acquisition result to obtain a video frame image set, wherein each image in the video frame image set has time and acquisition position identification; A coincident image calibration module, configured to perform coincident image calibration according to the acquisition angle of the camera and the video frame image set, perform feature point selection according to a first coincident image calibration result, and obtain a first feature point set; A texture feature analysis module, configured to perform texture feature analysis on the same time frame images in the video frame image set to obtain a first texture feature set; A rectification transformation module, configured to obtain first rectification transformation parameters according to a first target view angle and the camera; A fusion transformation module, configured to perform fusion transformation of the video frame image set based on the first feature point set and the first texture feature set according to the first rectification transformation parameters to obtain a first fusion result; A monitoring management module, configured to perform monitoring management of the target position according to the first fusion result.

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