A camera device deployment method, apparatus and readable storage medium

Abstract

This application belongs to the field of equipment deployment technology and discloses a method, apparatus, and readable storage medium for deploying camera equipment. The method includes: acquiring scene images from different perspectives collected by multiple camera devices within a deployment area; performing content comparison on each scene image and spatial correlation analysis based on the comparison results to determine the first location information of each camera device in the actual physical space; acquiring a deployment scheme design drawing, the design drawing containing virtual nodes of multiple camera devices, each virtual node having corresponding second location information and a unique virtual identifier; for each camera device, binding each camera device to a corresponding virtual identifier based on the matching relationship between its first location information and a corresponding second location information; wherein, the matching relationship is determined at least based on the spatial consistency between the first location information and the second location information. This application can save a significant amount of manpower and improve the accuracy and operational efficiency of equipment binding.

Description

Technical Field

[0001] This application relates to the field of equipment deployment technology, and in particular to a method, apparatus and readable storage medium for deploying camera equipment. Background Technology

[0002] In professional audio-visual systems, camera equipment, as the core acquisition device, typically relies on extensive professional knowledge and a large amount of manual operation for intelligent recommendation and deployment, a process that is cumbersome and inefficient. Specifically, during the camera equipment binding and configuration distribution phase, users need to manually locate the deployed virtual camera equipment through an online tool, and then manually control the corresponding physical camera equipment on-site to activate indicator lights, ultimately completing the binding between the physical and virtual camera equipment. Furthermore, users usually install the equipment based on the design drawings generated by the software. However, due to on-site environmental limitations (such as offset wall socket locations, fire sprinkler head obstructions, air conditioning duct interference, etc.), the equipment installation positions need to be adjusted according to the actual situation. After installation, the deployed equipment still needs to be measured and recorded a second time, and the positions and parameters of the corresponding equipment in the tool's design drawing need to be manually adjusted before the configuration is distributed to the actual camera equipment to complete the overall deployment.

[0003] The aforementioned process not only heavily relies on manual operation by on-site professionals, making remote control difficult and resulting in low deployment efficiency, but also carries the risk of binding errors due to human error. Therefore, achieving automatic binding between the physical and virtual locations of camera equipment has become a pressing technical challenge. Summary of the Invention

[0004] This application provides a method, apparatus, and readable storage medium for deploying camera equipment, thereby improving the accuracy and operational efficiency of equipment binding.

[0005] In a first aspect, embodiments of this application provide a method for deploying a camera device, the method comprising: Acquire scene images from different perspectives captured by multiple camera devices within the deployment area; Content comparison is performed on images of each scene, and spatial correlation analysis is conducted based on the comparison results to determine the first position information of each camera device in the actual physical space; Obtain the deployment scheme design diagram, which contains virtual nodes of multiple camera devices. Each virtual node has corresponding secondary location information and a unique virtual identifier. For each camera device, based on the matching relationship between its first location information and a corresponding second location information, each camera device is bound to a corresponding virtual identifier; The matching relationship is determined at least based on the spatial similarity between the first location information and the second location information.

[0006] Furthermore, content comparison is performed on the images of each scene, and spatial correlation analysis is conducted based on the comparison results to determine the first location information of each camera device in the actual physical space, including: Feature extraction is performed on scene images from different perspectives; Based on the features of images from different perspectives, feature consistency matching and feature completion are performed to obtain global image features; By performing correlation analysis between global image features and actual physical space, a coordinate mapping is generated to obtain the 3D coordinates of each pixel.

[0007] Furthermore, the global features of the image are correlated with the actual physical space to generate coordinate mappings, including: Based on the coordinate mapping of the actual physical space of the camera equipment in the deployment scheme design drawing, a third location information is generated; If the third location information is inconsistent with the second location information, the second location information shall be updated with the third location information.

[0008] Furthermore, the method also includes: obtaining the deployment parameters of each virtual node in the design diagram of the deployment scheme, and sending the deployment parameters to the corresponding camera device based on the binding relationship between the camera device and the virtual identifier; the deployment parameters include the location of the virtual node and the camera device parameters.

[0009] Furthermore, the method also includes: Obtain the virtual viewpoint corresponding to each virtual node, as well as the actual viewpoint currently captured by each camera device; The actual viewpoint of the camera device corresponding to the matching relationship is compared with the virtual viewpoint of the virtual node to determine whether the two match. If there is no match, the matching relationship between the camera equipment deployment and the virtual node is re-established. Based on the updated matching relationship, the camera equipment is bound to a corresponding virtual identifier to obtain the updated deployment parameters. Send the updated deployment parameters to the corresponding camera devices.

[0010] Furthermore, the method also includes: If the actual viewpoint of the camera device still cannot match the corresponding virtual viewpoint after the deployment parameters of the camera device are updated, an error message indicating that the viewpoint of the camera device has failed to match will be generated. The error message includes at least one of the following: the virtual identifier currently bound to the camera device, the actual deployment coordinates, and the viewing angle deviation data.

[0011] Furthermore, the method also includes: The deployment parameters of each virtual node, the actual viewing angle of each camera device, and the corresponding virtual viewing angle are obtained and sent to the display module. The display module receives and displays deployment parameters, actual viewpoint, and virtual viewpoint. At the same time, it establishes an association mapping between deployment parameters and viewpoint images in the display interface, and presents the correspondence between deployment parameters and viewpoint images in a visual manner based on the association mapping.

[0012] Secondly, embodiments of this application provide a camera equipment deployment device, the device comprising: The image acquisition module is used to acquire scene images from different perspectives from multiple camera devices within the deployment area; The first processing module is used to compare the content of each scene image and perform spatial correlation analysis based on the comparison results to determine the first position information of each camera device in the actual physical space. The second processing module is used to obtain the deployment scheme design diagram. The design diagram contains virtual nodes of multiple camera devices. Each virtual node has corresponding second location information and a unique virtual identifier. The third processing module is used to bind each camera device to a corresponding virtual identifier based on the matching relationship between its first location information and a corresponding second location information; wherein the matching relationship is determined at least based on the spatial consistency between the first location information and the second location information.

[0013] Furthermore, the first processing module is specifically used to extract features from multiple scene images captured by the same camera device to obtain global image features; perform correlation analysis between the global image features and the actual physical space to generate coordinate mapping and obtain the 3D coordinates of each pixel; and determine the first position information of the camera device in the actual physical space based on the 3D coordinates of each pixel.

[0014] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the camera device deployment method as described in any one of claims 1 to 7.

[0015] In summary, compared with the prior art, the beneficial effects of the technical solution provided in this application include at least the following: This application provides a method for deploying camera equipment. The method involves acquiring scene images from multiple different perspectives using camera equipment, performing content comparison and spatial correlation analysis, converting the images into the actual device coordinates of the camera equipment and mapping them to the coordinate system of the deployment design drawing, and then matching the actual device coordinates of the camera equipment with the virtual IDs in the virtual design drawing to obtain the deployment parameters of each camera equipment.

[0016] This application breaks through the problem of the disconnect between design drawings and actual site conditions in traditional deployments. Compared with the method of manually adjusting the equipment position by referring to the drawings based on experience, it can avoid the error of manual estimation and save the tedious steps of manual verification.

[0017] By comparing content and analyzing spatial correlations of multi-view scene images, the actual physical location of each camera device is automatically identified. This, combined with the location information of virtual nodes in the deployment scheme design drawing, establishes a correspondence between actual and virtual locations. Ultimately, this achieves automated binding between the actual physical location of the camera device and its virtual design drawing location, ensuring precise matching between the actual deployment coordinates and the design drawing coordinate system, and guaranteeing that the camera device deployment location perfectly matches the design expectations. Furthermore, this method establishes a unique correspondence between physical devices and virtual identifiers by binding the actual deployment coordinates to preset virtual IDs, and provides users with visualized deployment parameters for each camera device, facilitating parameter verification. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a camera device deployment method provided as an exemplary embodiment of this application.

[0019] Figure 2 A flowchart of another camera device deployment method provided as an exemplary embodiment of this application.

[0020] Figure 3a and Figure 3b The following are display effect diagrams of two simulation diagrams provided for an exemplary embodiment of this application.

[0021] Figure 4 An image captured by a camera device provided as an exemplary embodiment of this application.

[0022] Figure 5 This is a structural diagram of a camera equipment deployment apparatus provided as an exemplary embodiment of this application.

[0023] Figure 6 A structural diagram of another camera equipment deployment apparatus provided as an exemplary embodiment of this application. Detailed Implementation

[0024] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0025] Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] In traditional camera deployment processes, device binding and configuration rely heavily on manual operation by on-site professionals. Staff must first locate pre-defined virtual devices one by one in online tools, then manually control the actual devices on-site to confirm the correspondence, and finally bind the physical devices to the virtual identifiers. This entire process is not only time-consuming and labor-intensive but also highly susceptible to errors due to human negligence. Furthermore, although design drawings pre-plan device locations, on-site construction often requires temporary adjustments to the camera installation locations due to factors such as offset socket locations, obstructions from fire extinguishers, and interference from air conditioning ducts. In such cases, staff must remeasure the adjusted device coordinates on-site and then manually correct the corresponding virtual device parameters in the design tools. This is not only inefficient but can also lead to deviations between the actual deployment and the design plan due to measurement errors. These problems are amplified, especially in large venues and scenarios with densely deployed multiple cameras, severely impacting deployment progress and system reliability.

[0027] The industry has long regarded the static output of design schemes and passive adaptation during on-site execution as the standard process for camera equipment deployment. Technological development has focused on optimizing the visualization effects of design drawing tools or improving the imaging accuracy of equipment hardware, but has failed to systematically address the core pain points of the disconnect between design parameters and on-site conditions, as well as the inefficient mapping between physical equipment and virtual markers. This has led to a path dependency on the traditional process framework. This inertia ignores the constraints that dynamic changes in environmental variables in actual deployment scenarios place on overall efficiency. As a result, the design phase cannot predict potential on-site interference factors, and the on-site execution phase lacks real-time data interaction with the design system. Ultimately, the deployment effect relies on repeated corrections in the later stages, making it difficult to guarantee stability and timeliness.

[0028] Against this backdrop, the inventors realized that the real technological breakthrough lay not simply in improving the functionality of design tools or the hardware precision of equipment, but in constructing an automated closed loop encompassing three key stages: design scheme, on-site construction, and automatic binding and interactive adjustment. This would allow the digital parameters of the design scheme to directly guide equipment installation during on-site construction. Simultaneously, the actual location data of the equipment acquired during on-site construction could be automatically synchronized to the virtual system, achieving precise binding between physical equipment and virtual identifiers. Data flowed in real time and supported each other across these three stages, requiring no manual intervention to connect them, forming a complete automated process from design to implementation to virtual mapping, breaking down the information barriers between design and actual deployment in traditional processes.

[0029] Based on this, the camera equipment deployment location calculation and feedback method provided in this application realizes the automated connection from design scheme to on-site deployment by acquiring multi-view images, automatically calculating actual coordinates and mapping them to design drawings, intelligently binding virtual IDs, and interactively adjusting. It not only saves the tedious steps of manual verification and correction, but also reduces the risk of binding errors through the perspective comparison and verification mechanism. Especially in multi-device deployment scenarios such as conference rooms and large venues, it can significantly improve deployment efficiency and accuracy, providing a brand-new solution for intelligent deployment of camera equipment.

[0030] Please see Figure 1 This application provides a method for deploying a camera device, which specifically includes the following steps: Step S110: Obtain scene images from different perspectives captured by multiple camera devices within the deployment area.

[0031] By deploying multiple cameras within a region to simultaneously acquire scene images from different perspectives, the core objective is to provide multi-dimensional visual data support for subsequent spatial correlation analysis and location calculation. These scene images refer to two-dimensional visual data captured by multiple cameras within the deployment area at the exact same time, from their respective different physical locations and shooting angles, fully reflecting the physical environmental characteristics of the area. Compared to single-view images, which only reflect two-dimensional planar information, multi-view images can capture the visual differences of the same physical point in the scene under different observation angles. These differences are crucial raw data for subsequently determining the physical location of the equipment.

[0032] Step S120: Compare the content of each scene image and perform spatial correlation analysis based on the comparison results to determine the first position information of each camera device in the actual physical space.

[0033] By converting two-dimensional image information into the three-dimensional physical coordinates of the camera device (i.e., the first position information), a precise mapping from image to spatial position is achieved. After receiving the multi-view scene images sent in step S110, feature extraction, spatial correlation analysis, and coordinate calculation are required to finally obtain the 3D coordinates of each camera device in the actual physical space.

[0034] Step S130: Obtain the deployment scheme design diagram. The design diagram contains virtual nodes of multiple camera devices. Each virtual node has corresponding second location information and a unique virtual identifier.

[0035] The system supports multiple channels for obtaining deployment scheme design drawings. In some embodiments, the system supports retrieving pre-completed preset design drawings. This can be achieved by interacting with professional design software such as AutoCAD and Visio to directly obtain the stored complete deployment scheme design drawings, or by batch importing design drawing-related data in text format to quickly retrieve and load the design drawings. In other embodiments, the system also supports generating customized design drawings in real time based on the actual site environment. Specifically, based on the user's personalized deployment preferences and actual needs, the user can input basic information of the blank site, deployment functional requirements, or directly select suitable scene type templates such as conference rooms. The system combines the actual site location cloud data collected on-site with AI algorithms for intelligent analysis and optimization planning, automatically generating the optimal layout design drawing of virtual nodes for equipment such as cameras. At the same time, based on the user's actual deployment needs, the system matches and configures the corresponding optimal deployment parameter design results for each virtual node, ensuring that the design drawing is highly adapted to the site environment and user needs.

[0036] Step S140: For each camera device, bind each camera device to a corresponding virtual identifier based on the matching relationship between its first location information and a corresponding second location information; wherein, the matching relationship is determined at least based on the spatial matching degree between the first location information and the second location information.

[0037] The system binds the actual physical location information of each camera device to the second location information of a matching virtual node, using the spatial distance between the two as the core criterion for determining the matching relationship. After identifying the matching object, the virtual identifier of the virtual node is bound to the current camera device, generating a binding relationship between the camera device hardware and the virtual identifier. Based on spatial conformity, the system automatically binds the physical camera device to the virtual node in the design drawing, achieving high-precision spatial matching between the actual device coordinates and the deployment design drawing. This method significantly improves the accuracy and consistency of camera device deployment, reduces errors and time consumption caused by manual intervention, and ensures a high degree of conformity between the actual deployment and the design drawing. Simultaneously, it simplifies the deployment process, enhances the reliability and maintainability of the system deployment, and provides solid technical support for the overall deployment work.

[0038] For example, each camera device can be bound to a corresponding virtual identifier by calculating the coordinate difference between the first location information and each second location information, selecting the camera device with the smallest difference corresponding to the first location information, and binding that camera device to the corresponding virtual identifier.

[0039] For example, each camera device can be bound to a corresponding virtual identifier. This can be achieved by extracting the feature point set of the actual images captured by the camera device and calculating the correlation degree between this set and the set of virtual feature points within a preset coverage area of ​​each virtual node. Examples include the percentage of overlapping feature points and the average relative positional deviation. The virtual node with the highest correlation degree is then selected, and the camera device is bound to the corresponding virtual identifier.

[0040] For example, each camera device can be bound to a corresponding virtual identifier by calculating the overlap between the actual viewpoint of the camera device and the virtual viewpoint of each virtual node, selecting the corresponding virtual node with the acceptable overlap, and then binding the camera device to the corresponding virtual identifier.

[0041] In some embodiments, the actual deployment coordinates of the camera device can be bound to a preset virtual identifier by adopting a location nearest matching rule. This can effectively address the calculation errors that may exist in the actual deployment coordinates of 3D reconstruction, as well as the problem of low coordinate difference distinction caused by the close distribution of camera devices in the actual scene, thereby reducing the risk of misjudgment in binding. At the same time, this rule initially locks the camera devices to be bound with a simple matching logic. While ensuring the accuracy of binding, it avoids redundant calculations caused by complex algorithms, significantly improves the binding processing speed, and achieves the dual effect of accurate matching and efficient operation.

[0042] The above embodiments provide a camera equipment deployment method. This method achieves precise positioning of the physical location of the camera equipment through multi-view image analysis, and completes automated and precise binding between the physical equipment and virtual nodes by combining the deployment design diagram. This can significantly improve the positioning accuracy and binding efficiency of camera equipment deployment, reduce errors and time consumption caused by manual operation, and ensure that the actual deployment is highly consistent with the design scheme. At the same time, it simplifies the deployment process, enhances the reliability and maintainability of the system deployment, and provides full-process technical support for the standardized and intelligent deployment of camera equipment.

[0043] Content comparison is performed on the images of each scene, and spatial correlation analysis is performed based on the comparison results to determine the first position information of each camera device in the actual physical space. The above step S120 includes the following sub-steps (steps S1201-S1204): Step S1201: Extract features from scene images from different perspectives; From multi-view scene images, the system automatically identifies and extracts "feature points" that represent key information of the scene, such as wall corners, table and chair outlines, and door and window edges. These visual features are then converted into numerical data that can be recognized by computers, laying the foundation for subsequent comparison of images from different perspectives and establishing spatial relationships. This is equivalent to labeling each image with "key recognition markers".

[0044] Step S1202: Based on feature consistency matching and feature completion of images from different viewpoints, obtain the global features of the image; First, compare the feature points of images from different perspectives to find the corresponding feature points of the same physical location under different perspectives (such as the corresponding features of "the front door of the conference room" in front and side images), and complete the feature consistency matching. Then, complete the feature points missing from a single perspective due to occlusion or angle issues. Finally, integrate all the matched and completed feature points to form a global image feature covering the entire deployment scene. This eliminates the information gaps caused by the inability to directly obtain continuous, complete and consistent scene space and feature information from multi-view images due to the limitations of different camera devices' perspectives, occlusion or missing feature matching.

[0045] Step S1203: Based on the correlation analysis between the global features of the image and the actual physical space, generate coordinate mapping to obtain the 3D coordinates of each pixel; By combining the actual physical information of the deployment scenario (such as the length and height of the conference room), the relative positional relationship of each feature point in the global features of the image is analyzed, and a correspondence rule (coordinate mapping) between the image pixel position and the three-dimensional coordinates of the actual physical space is established. Based on this rule, the precise 3D coordinates of each pixel in the image in the actual space are obtained, realizing the transformation from "two-dimensional image" to "three-dimensional space".

[0046] Step S1204: Determine the first position information of each camera device in the actual physical space based on the 3D coordinates of each pixel.

[0047] In images captured by camera equipment, the 3D coordinates of all pixels are radially distributed with the camera lens as the "origin". An algorithm is used to reverse-engineer the common intersection point of these radial rays; this intersection point represents the actual physical position of the camera lens. Combined with information such as the equipment's installation height and bracket position, the complete three-dimensional coordinates of each camera device in actual space are ultimately determined—this is the initial position information.

[0048] In this embodiment, the deployment scenario is a conference room, with 2-5 camera devices deployed within the scene. The camera devices are sparsely distributed with significantly large baseline distances. To achieve accurate calculation of the actual device coordinates in this scenario, this application adopts the following specific implementation scheme: Specifically, firstly, 2-5 cameras deployed in the conference room are used to simultaneously acquire multi-view 2D images, ensuring that all images are acquired at the same time. This uniformity of image acquisition time ensures the complete restoration of the deployed scene and avoids scene information deviations caused by time differences. Secondly, the DUST3R model in 2D to 3D reconstruction technology is used to process the acquired multi-view 2D images. This model is based on a large-scale VisionTransformer (ViT) architecture and proposes an innovative dense 3D representation (pointmap), which can accurately predict the corresponding 3D spatial coordinates for each image pixel, thereby efficiently capturing the geometric relationships between images.

[0049] The specific processing steps are as follows: First, feature data is extracted using the ViT architecture. Then, the strong correlation learning capability of the Transformer large model is used to mine the geometric consistency across views, performing consistency matching and feature completion on the features of images from different perspectives. After processing the feature data of all images from different perspectives, the final global image features are generated. The global image features can completely cover all necessary feature data in the conference room, and the feature data has consistency. Finally, based on the global features, the 3D coordinates of each pixel are directly predicted and dense point cloud data (dense pointmap) is output. Based on this point cloud data, the actual device coordinates of the camera equipment can be accurately deduced.

[0050] In particular, this application addresses several technical challenges in calculating the coordinates of camera devices in a conference room deployment scenario with sparse baseline distribution and few texture features. From a technical classification perspective, calculating device positions through multi-view images falls under the category of Multi-View Stereo (MVS). However, traditional MVS technology is only suitable for scenarios with densely distributed camera devices, high image overlap, and rich scene textures (such as tourist attraction modeling and professional video recording). In contrast, the conference scenario in this application not only has a small number of camera devices (2-5), sparse distribution, and large baselines, but also suffers from sparse textures and high repetition (such as large areas of white walls and identical chairs), which completely exceeds the adaptability of traditional MVS technology. Meanwhile, traditional manual layout methods are heavily reliant on camera equipment parameters and explicit features, making accurate matching impossible in low-texture environments like conference scenes. These factors collectively constitute a recognized technical challenge in the industry. This application, however, successfully overcomes these limitations by innovatively using the DUST3R model. This model, based on a large-scale VisionTransformer (ViT) architecture, does not rely on preset camera equipment parameters or explicit feature matching. It precisely addresses the pain points of lacking explicit features for matching and the difficulty in preset camera equipment parameters in conference scenes. Even under extreme conditions of low texture, large baseline, and sparse viewpoints, it can still achieve accurate 3D coordinate prediction through deep mining of cross-view geometric consistency, ultimately realizing high-precision calculation of camera equipment coordinates. This overcomes the inherent defects of traditional methods, improves the automation and processing efficiency of 3D reconstruction, and provides an efficient and reliable solution for intelligent deployment of camera equipment in conference scenes.

[0051] In some embodiments, a third location information is generated based on the coordinate mapping of the actual physical space of the camera device in the deployment scheme design drawing; if the third location information is inconsistent with the second location information, the second location information is updated with the third location information.

[0052] The equipment coordinates are mapped onto the coordinate system of the deployment design drawing. Adjustments are made to each coordinate in the deployment design drawing, and a coordinate transformation algorithm is used to align the actual equipment coordinates with the coordinate system of the deployment design drawing, thus obtaining accurate actual deployment coordinates. This adjustment process is based on preset mapping rules and automatically corrects coordinate deviations, ensuring that the actual deployment location perfectly matches the design intent and improving subsequent processing efficiency.

[0053] In this process, the actual equipment coordinates are obtained based on 3D reconstruction, while the coordinate system of the deployment design drawing is a preset digital benchmark. There is an inherent difference in spatial dimensions and mapping deviation risk between the two. Especially in scenarios such as conference rooms where there are relatively many interconnected devices and high requirements for device positioning accuracy, how to achieve precise alignment between physical spatial coordinates and digital design coordinate systems, and avoid positioning offset caused by different coordinate system benchmarks, is the technical challenge that needs to be overcome in this step. Through the above coordinate mapping operation, the spatial alignment between the physical camera equipment position and the digital design drawing is finally achieved, eliminating the positioning deviation caused by coordinate system differences, ensuring the consistency between the actual deployment and the original design drawing, and improving the reliability of system deployment.

[0054] Figure 2 For a flowchart illustrating another camera device deployment method provided in this application embodiment, please refer to [link / reference]. Figure 2 In some embodiments, the above-described camera deployment method further includes the following step (step S150): Step S150: Obtain the deployment parameters of each virtual node in the design diagram of the deployment scheme, and send the deployment parameters to the corresponding camera device based on the binding relationship between the camera device and the virtual identifier; the deployment parameters include the location of the virtual node and the camera device parameters.

[0055] In this process, the actual deployment coordinates of the camera equipment and various preset deployment coordinates are processed to bind the preset virtual ID of the camera equipment to the actual deployment coordinates. This process needs to achieve efficient matching in coordinate data with potential errors, while ensuring the unique correspondence between the virtual ID and the physical device. This places high demands on the robustness of the matching logic. Through the above processing, efficient matching operations can be achieved, thereby achieving accurate binding between the virtual and the real. This ensures the rapid identification and precise positioning of the camera equipment in the overall system, further improving the automation level of equipment management and the reliability of deployment. It avoids errors and delays that may occur due to manual binding and provides a stable foundation for subsequent system operations.

[0056] In this embodiment, the camera equipment parameters include, but are not limited to: the actual spatial location of the camera equipment, pitch angle, yaw angle, focal length, shooting angle, and coverage area. These deployment parameters comprehensively present the core operation and spatial status of the camera equipment. On the one hand, the camera's shooting orientation can be quickly calibrated based on pitch and angle parameters, avoiding repeated adjustments due to ambiguous parameters. On the other hand, complete parameter information helps users intuitively determine whether the camera equipment deployment meets the scene requirements, eliminating the need for additional on-site surveys and significantly improving the efficiency of deployment effect verification. Simultaneously, it provides clear data support for subsequent system maintenance and parameter optimization, further reducing manual operation costs and errors.

[0057] The aforementioned method acquires and processes images from multiple perspectives, mapping their actual device coordinates to virtual IDs in a virtual design drawing to obtain deployment parameters for each camera device. These deployment parameters comprehensively cover key parameters of the camera devices and are associated with corresponding virtual IDs (such as IP addresses or MAC addresses), forming a complete data loop of position, attitude, and identification. This provides accurate data support for subsequent device management, parameter adjustment, and scene simulation. This application eliminates the tedious manual verification process. By comparing the content of multi-view scene images, analyzing spatial correlations, and combining virtual node information in the deployment design drawing, it automatically identifies the correspondence between the actual and virtual positions of each camera device. This achieves automated binding of the actual physical location of the camera device to its virtual design drawing position, completing intelligent deployment operations, saving significant manpower, and improving the accuracy and efficiency of device binding.

[0058] In some embodiments, the method further includes obtaining, for each camera device that has completed the initial binding of actual deployment coordinates and camera device virtual ID, the virtual viewpoint corresponding to each camera device and the actual viewpoint currently being captured by each camera device; The actual viewpoint of the camera device corresponding to the matching relationship is compared with the virtual viewpoint of the virtual node to determine whether the two match. If there is no match, the matching relationship between the camera equipment deployment and the virtual node is re-established. Based on the updated matching relationship, the camera equipment is bound to a corresponding virtual identifier to obtain the updated deployment parameters. Send the updated deployment parameters to the corresponding camera devices.

[0059] For example, based on the comparison between the actual viewpoint of the camera device corresponding to the matching relationship and the virtual viewpoint of the virtual node, the following quantification rules are used to determine whether the two match: The overlap ratio of the shooting range is used to determine the overlap. Specifically, image recognition technology is used to extract the shooting coverage boundary of the actual viewpoint and the design coverage boundary of the virtual viewpoint, and the overlap ratio between the two in the deployment scheme design drawing is calculated. For example, when the accuracy requirement for the scene is 80%-90%, if the overlap ratio is ≥85%, it is initially determined to be a viewpoint match; if the overlap ratio is <85%, it is directly determined to be a viewpoint mismatch. Furthermore, for example, if the deployment scheme design drawing has a key shooting area, based on the key shooting area marked in the design drawing, such as the conference room speaker's seat or the main stage area, the overlap of the actual viewpoint and the virtual viewpoint in the key area is calculated and combined with the overlap ratio of the shooting range to jointly determine whether the two match. For example, when the scene requirement is between 90% and 95%, if the overlap of key areas is ≥90% and the overlap ratio of shooting range is ≥85%, it is judged as a viewpoint match; if the overlap of key areas is <90%, even if the overlap ratio of shooting range meets the standard, it is still judged as a viewpoint mismatch.

[0060] If the above rules determine that the viewpoint is mismatched, the matching relationship between the camera device deployment and the virtual node is re-established. Based on the updated matching relationship, the camera device is bound to a corresponding virtual identifier to obtain the updated deployment parameters. The updated deployment parameters are then sent to the corresponding camera device.

[0061] For example, in this embodiment, threshold adjustment can be triggered in two ways: First, the user selects a preset threshold template according to the scene type in the scene settings interface of the deployment tool, and the system automatically loads the corresponding threshold range; second, the user manually enters a custom threshold (which must be within the above threshold range), and clicks the trigger button after entering it. The system synchronizes the new threshold to the view matching judgment module, and subsequent judgments are all performed based on the new threshold. All adjustment records will be automatically stored in the system log and can be viewed retrospectively.

[0062] In this embodiment, if the calculation error of the camera device position after 3D reconstruction is large, or if the actual deployed camera devices are too close to be distinguishable, it may lead to errors in the automatic binding result, i.e., the actual deployed camera devices are mismatched with the preset camera devices in the design drawing. Therefore, the virtual and actual viewpoints of each camera device that has completed the initial binding are compared. When the virtual and actual viewpoints do not match, rebinding is triggered. This can effectively identify the inconsistency between the virtual and actual viewpoints caused by on-site deployment deviations or identification errors, significantly improving the accuracy of binding the camera device's virtual ID to the actual deployment coordinates and reducing the cost of manual troubleshooting. At the same time, after completing the accurate binding of the actual deployment coordinates and virtual IDs, the preset parameters corresponding to the current design drawing are synchronously sent to the corresponding camera devices to achieve automatic updating and configuration of deployment parameters.

[0063] In some embodiments, the method further includes adjusting deployment parameters based on the current design drawing matching effect if no binding error is found during rebinding. When it is confirmed that the effect after adjusting the parameters matches the current design drawing matching effect, the updated deployment parameters are sent to the corresponding camera device.

[0064] By matching and calibrating the actual viewpoint with the corresponding virtual viewpoint, a dual verification and dynamic correction mechanism for deployment binding is constructed, fundamentally improving the accuracy and reliability of binding the physical location of the camera device with its virtual ID. This avoids subsequent parameter configuration failures caused by initial binding errors. Simultaneously, when a viewpoint mismatch is detected, rebinding is automatically triggered, eliminating the need for manual troubleshooting and locating of problematic devices, significantly reducing the time cost and operational complexity of manual intervention. The updated deployment parameters generated after rebinding can correct the correspondence between the virtual ID and the actual device in real time, ensuring that the virtual identifier, deployment coordinates, and functional parameters of each camera device remain consistent with the physical device. This lays a precise data foundation for the stable operation of the subsequent audio-visual system, improving the intelligence and fault tolerance of the overall deployment process. Furthermore, in cases where the effect still does not match after correct binding due to internal device errors, further optimization and adjustments can be made to ensure that the presentation effect meets expectations.

[0065] For example, if the actual viewpoint of the camera device still cannot match the corresponding virtual viewpoint after the deployment parameters of the camera device are updated, an error message indicating that the viewpoint of the camera device has failed to match is generated; the error message includes at least one of the virtual identifier currently bound to the camera device, the actual deployment coordinates, and the viewpoint deviation data.

[0066] The error message mentioned above contains information such as the virtual ID of the camera device (e.g., IP address or MAC address), which helps users quickly identify the incorrectly identified device and thus speeds up the processing.

[0067] In some embodiments, this application can send error messages to the display module. The generated error messages indicating camera device perspective mismatch can be intuitively presented through the display module. This not only enables timely detection and accurate location of binding anomalies but also solves the technical pain point of hidden faults in traditional deployments—the virtual IDs (IP addresses / MAC addresses) explicitly carried in the error messages allow users to quickly identify camera devices with matching problems without having to check every single device, avoiding the time wasted on blind troubleshooting. Simultaneously, combined with the previously presented visualized deployment scenarios (such as design icon annotations and device location distribution) on the display module, users can intuitively associate the correspondence between the abnormal device ID, the actual deployment location, and the virtual design node, clearly determining whether the root cause of the fault is 3D reconstruction coordinate errors, on-site installation deviations, or ID binding logic anomalies. This significantly reduces the technical threshold and operational complexity of fault diagnosis, enabling users to locate fault points more quickly and take targeted solutions, significantly shortening the fault handling cycle, reducing deployment downtime caused by binding anomalies, further improving system operation and maintenance efficiency and deployment process stability, and ensuring that the overall deployment work can be efficiently advanced and implemented on time.

[0068] This visualization mode not only facilitates real-time verification of the binding results during deployment but also helps users quickly pinpoint the core issues causing binding errors. For example, significant discrepancies in the shooting range and angle between the actual and virtual perspectives can directly indicate ID binding misalignment or abnormal parameter configuration. If the image content is completely unrelated, it can initially indicate a hardware malfunction or 3D reconstruction coordinate error. Compared to traditional debugging methods that rely solely on data verification, this visual comparison mechanism significantly reduces the difficulty and time cost of fault identification, effectively improves system debugging efficiency, and significantly shortens the fault diagnosis cycle. It creates a closed loop from parameter calculation and binding matching to fault diagnosis, correction, and optimization, further enhancing the practicality and operability of the overall deployment solution and providing users with a more efficient and convenient deployment and debugging experience.

[0069] In some embodiments, the following step (step S170) is also included: Step S170: Obtain the deployment parameters of each virtual node, the actual viewing angle of each camera device and the corresponding virtual viewing angle, and send them to the display module; The display module receives and displays deployment parameters, actual viewpoint, and virtual viewpoint. At the same time, it establishes an association mapping between deployment parameters and viewpoint images in the display interface, and presents the correspondence between deployment parameters and viewpoint images in a visual manner based on the association mapping.

[0070] As the core carrier of human-computer interaction, the display module can uniformly receive and integrate all camera equipment deployment parameters, and comprehensively display the deployment parameters in an intuitive way through design drawing overlay annotations, 3D scene restoration, parameter lists, etc. At the same time, it will also send the camera equipment deployment parameters, actual viewpoints and virtual viewpoints to the display module simultaneously, and complete the visualization presentation in the form of dual-window split-screen comparison and same scene overlay annotation, so as to build an intuitive comparison and efficient verification mechanism for deployment matching for users.

[0071] This centralized visualization mode breaks through the limitations of traditional camera equipment deployment, where parameters are scattered and inconvenient to view. Users can fully grasp the overall deployment without relying on switching between multiple tools or manual organization, significantly improving the accessibility of deployment parameters. During deployment, users can verify the installation accuracy of each device in real time. By directly observing the difference between the actual and virtual perspectives, they can quickly check whether the shooting range overlaps, whether the target scene matches, and whether the image angle is consistent. This allows for timely detection of issues such as coordinate deviations and ID binding errors. Without the need for professional measurement tools or complex data calculations, users can accurately determine the degree of fit between the actual deployment status of the camera equipment and the design expectations. At the same time, this visualization method greatly reduces the reliance on the professional skills of maintenance personnel. When perspective matching errors occur, users do not need to debug each camera device on-site. They can quickly locate the cause of the deviation simply by observing the difference between the two perspectives, avoiding the time wasted by blind operation and effectively reducing the complexity and cost of manual troubleshooting.

[0072] During the acceptance phase after deployment, this visual presentation format can also provide clear and intuitive verification criteria. Users can directly and intuitively confirm whether the actual field of view of each camera device meets the design requirements through the display module, without the need for additional third-party tools or on-site surveys. This significantly shortens the deployment and acceptance cycle, enhances the controllability and reliability of the entire deployment process, and ensures that the deployment effect of the camera equipment is highly consistent with the design expectations.

[0073] In some embodiments, this application can adjust the pitch and yaw angles of the camera device to achieve the best coverage and viewing angle; and can also set preset camera positions and modes for multiple camera devices, thereby making user operation more convenient.

[0074] In some embodiments, during actual deployment and installation, drilling holes in fixed structures such as walls and ceilings is often required to install brackets to secure the camera equipment. Once installed, the physical position of the camera equipment is rigidly fixed and cannot be adjusted arbitrarily. The automatic calibration process of this application, however, is based on automatic system binding and adjustment. By adjusting the virtual node positions and camera equipment parameters in the deployment scheme design drawing, the actual camera equipment layout matches the scheme design drawing, ensuring that the camera equipment's shooting effect is consistent with the design expectations.

[0075] In some embodiments, if, after automatic calibration is completed, the user needs to modify the shooting effect of the camera device due to changes in the use of the meeting room, such as changing from a regular meeting to a multi-person seminar, live speech, or adjusting the scene layout, such as rearranging tables and chairs or adding a display area, such as expanding the coverage of a certain area, focusing on a specific target area, or adjusting the screen angle to avoid new obstructions, but is limited by the fact that the physical position of the device is fixed and cannot be changed, and users usually do not have the professional technical knowledge to manually adjust complex parameters, this application designs a flexible calibration compensation mechanism that is suitable for non-professional users.

[0076] When users have the above-mentioned shooting effect adjustment needs, they do not need to touch the actual equipment. They only need to directly modify the deployment scheme design in the visualization interface of the deployment tool. For example, they can drag the position of the corresponding virtual node in the design, select the desired template such as expanding the coverage area or focusing on the speaking area in the interface preset options, or manually adjust the viewing angle parameters of the virtual node.

[0077] In this calibration process, the current actual physical position of each camera device is first obtained. Since the hardware installation is fixed, this position remains unchanged. Based on this actual position, the coordinate position of the corresponding virtual node in the design drawing is adjusted in reverse to ensure that the coordinates of the virtual node are accurately aligned with the actual physical position of the device, thus avoiding the disconnect between the design drawing and the on-site equipment. Subsequently, combined with the user's modified design drawing requirements, such as the new viewing angle range and focus target of the virtual node, and the limitations of the actual physical position of the device, the appropriate deployment parameters are automatically recalculated and generated, including the tilt angle, coverage area, and shooting angle of the virtual node. These parameters will avoid the limitation that the physical position of the device cannot be changed, and by optimizing adjustable dimensions such as angle and focal length, the difference between the actual installation position and the new design requirements is offset.

[0078] Once new parameters are generated, they are automatically sent to the corresponding camera device, completing the synchronous update of device parameters without requiring manual configuration by the user. This achieves a closed loop where the system automatically calculates parameters and synchronizes new effects to meet the user's design changes, ensuring that the actual shooting effect of the camera device, such as coverage of the target area, image angle, and focus accuracy, perfectly matches the preset requirements of the user's modified design. Furthermore, adjusted virtual nodes in the design are highlighted with easily recognizable markers for non-professional users. For example, node icons are marked with a red label indicating synchronized devices, and the changed area covered by the node is highlighted with a yellow dashed box. A text description stating that the parameters have been adapted to the actual device position is also displayed when the mouse hovers over the node. This allows users to clearly understand the relationship between design modifications and actual device effects, lowering the operational threshold for non-professional users and preventing deployment logic confusion or effect deviations caused by design changes, thus balancing flexibility and ease of operation.

[0079] In this embodiment, the calibration process is triggered in two ways: one is manually triggered by the user clicking the trigger button on the interface; the other is automatically triggered by the system after detecting a modification to the design drawing. Both methods will initiate an automatic device calibration process.

[0080] In some embodiments, the actual deployed equipment may experience position or angle drift due to vibration or temperature differences during operation, resulting in inconsistencies with the deployment scheme design. This application incorporates an automatic checking mechanism that generates an error message indicating camera viewpoint mismatch when a difference exists between the actual and virtual viewpoints. Users can trigger the calibration process manually or automatically.

[0081] In some embodiments, the deployment tools accompanying this application support rich visualization operations and functional expansion: users can directly select any bound camera device in digital design drawings or simulated scenarios, and the system will generate and display the corresponding camera device distortion effect in real time based on the lens intrinsic parameters (such as focal length, distortion coefficient, etc.) of that device. This helps users predict the distortion of the actual captured image in advance; in simulated meeting scenarios, multi-image stitching is also supported. It can intelligently synthesize the shooting angles of multiple camera devices according to their actual deployment positions to form a panoramic view covering the entire conference room scene. It also provides intelligent framing function, which can automatically adjust the shooting range and image weight of each camera device according to the needs of the meeting scene (such as focusing on the speaker, covering all participants, etc.) to achieve the optimal visual presentation. In addition, the meeting real-scene mode provides users with a WYSIWYG parameter adjustment experience: users can adjust the core parameters of the camera devices such as tilt angle, yaw angle, and focal length in real time in this mode. The system will synchronously provide feedback on the corresponding actual shooting scene, allowing users to intuitively see the impact of parameter adjustment on the shooting effect, quickly determine the most suitable equipment configuration for the current meeting scene, and significantly improve the efficiency and accuracy of parameter configuration without repeated on-site debugging.

[0082] In some embodiments, users can view the scene by directly selecting the camera device (the actual scene captured by the associated camera device), or they can select the camera device through a simulation diagram to view the scene (simulated lens distortion effect, see Figures 3a and 3b for details); or they can directly view the scene captured by the actual associated camera device (see Figure 4 for details).

[0083] In some embodiments, to meet users' viewing needs in different scenarios, this application provides two flexible ways to view camera device images: The first is to directly select the physically associated camera device, that is, users can click on the identifier (such as virtual ID, device icon) of the target camera device in the device list or design diagram of the deployment tool. The system will retrieve and display the actual shooting image of the camera device in real time. At this time, the image content is completely consistent with the actual scene, which makes it easy for users to verify the actual working status, shooting range and image clarity of the device; The second is to select and view through a simulation diagram, that is, users can select any preset virtual node in the digital simulation scene, and the system will generate a simulated image with lens distortion effect based on the preset position, lens parameters and scene model of the node. This image is highly simulated with the shooting effect after the device is actually installed, which is suitable for effect prediction and parameter pre-adjustment before deployment, or when it is not possible to retrieve the actual image in real time (such as when the device is not powered on or the network is interrupted), to quickly understand the theoretical shooting range and visual effect of the device. The two viewing methods complement each other, further improving the flexibility and convenience of deployment operations.

[0084] In some embodiments, this application can update the position and orientation of the camera in the design drawing after receiving the actual installation location of the camera, complete automatic binding and configuration parameter updates, and ultimately ensure that the design drawing is consistent with the actual location. In the updated design, users can adjust the pitch and yaw angles of the camera through simulation to achieve optimal coverage and viewing angle; they can also set various preset camera positions and modes to obtain a simulation experience highly consistent with the actual deployment effect; in subsequent actual use, the equipment scheme debugged in the simulation can be directly applied (such as the camera automatically rotating to a preset angle).

[0085] Please see Figure 5 Another embodiment of this application provides a camera equipment deployment apparatus, the apparatus comprising: The image acquisition module 101 is used to acquire scene images from different perspectives from multiple camera devices within the deployment area; The first processing module 102 is used to perform content comparison on the images of each scene, and perform spatial correlation analysis based on the comparison results to determine the first position information of each camera device in the actual physical space. The second processing module 103 is used to obtain the deployment scheme design diagram. The design diagram contains virtual nodes of multiple camera devices. Each virtual node has corresponding second location information and a unique virtual identifier. The third processing module 104 is used to bind each camera device to a corresponding virtual identifier based on the matching relationship between its first location information and a corresponding second location information; wherein the matching relationship is determined at least based on the spatial matching degree between the first location information and the second location information.

[0086] In some embodiments, the first processing module 102 is used to extract features from scene images from different perspectives; perform feature consistency matching and feature completion based on the features of the images from different perspectives to obtain global image features; perform correlation analysis between the global image features and the actual physical space to generate coordinate mapping and obtain the 3D coordinates of each pixel; and determine the first position information of each camera device in the actual physical space based on the 3D coordinates of each pixel.

[0087] In some embodiments, the third processing module 104 specifically calculates the coordinate difference between the first location information and each second location information, selects the camera device corresponding to the first location information with the smallest difference, and binds the camera device to a corresponding virtual identifier.

[0088] In some embodiments, the third processing module 104 is specifically used to generate third location information based on the coordinate mapping of the actual physical space of the camera device in the deployment scheme design drawing; if the third location information is inconsistent with the second location information, the second location information is updated with the third location information.

[0089] In some embodiments, the third processing module 104 is specifically used to obtain the deployment parameters of each virtual node in the design diagram of the design deployment scheme, and send the deployment parameters to the corresponding camera device based on the binding relationship between the camera device and the virtual identifier; the deployment parameters include the location of the virtual node and the camera device parameters.

[0090] In some embodiments, the second processing module 103 is specifically used to obtain the virtual viewpoint corresponding to each virtual node; the first processing module 102 is used to obtain the actual viewpoint currently being captured by each camera device; the third processing module 104 compares the actual viewpoint of the camera device corresponding to the matching relationship with the virtual viewpoint of the virtual node to determine whether the two match; if they do not match, a new matching relationship between the camera device deployment and the virtual node is established, and the camera device is bound to a corresponding virtual identifier based on the updated matching relationship to obtain the updated deployment parameters; the updated deployment parameters are then sent to the corresponding camera device. The camera device is used to receive the updated deployment parameters and update its device parameters.

[0091] In some embodiments, if the third processing module 104 determines that the actual viewpoint of the camera device still cannot match the corresponding virtual viewpoint after the camera device updates its deployment parameters, it generates an error message indicating that the camera device viewpoint matching has failed.

[0092] For example, the error message may include at least one of the following: the virtual identifier currently bound to the camera device, the actual deployment coordinates, and the viewing angle deviation data.

[0093] Please see Figure 6 In some embodiments of this application, the device further includes a display module 105, which is used to receive and display error information generated by the third processing module 104 indicating that the camera device's perspective matching has failed.

[0094] In some embodiments, the display module 105 is used to receive and display the deployment parameters, actual viewpoint, and virtual viewpoint sent by the third processing module 104, and at the same time establish an association mapping between the deployment parameters and the viewpoint in the display interface, and present the correspondence between the deployment parameters and the viewpoint in a visual manner based on the association mapping.

[0095] The specific limitations of the camera equipment deployment device provided in this embodiment can be found in the embodiment of the camera equipment deployment method described above, and will not be repeated here. Each module in the above-described camera equipment deployment location calculation and feedback device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0096] Another embodiment of this application also provides a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the above-described camera device deployment method embodiments and achieve the same technical effects. To avoid repetition, it will not be described again here.

[0097] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for deploying camera equipment, characterized in that, include: Acquire scene images from different perspectives captured by multiple camera devices within the deployment area; Content comparison is performed on images of each scene, and spatial correlation analysis is conducted based on the comparison results to determine the first position information of each camera device in the actual physical space; Obtain the deployment scheme design diagram, which contains virtual nodes of multiple camera devices, and each virtual node has corresponding second location information and a unique virtual identifier; For each camera device, based on the matching relationship between its first location information and a corresponding second location information, each camera device is bound to a corresponding virtual identifier; The matching relationship is determined at least based on the spatial similarity between the first location information and the second location information.

2. The camera equipment deployment method according to claim 1, characterized in that, Content comparison is performed on the images of each scene, and spatial correlation analysis is conducted based on the comparison results to determine the first location information of each camera device in the actual physical space, including: Feature extraction is performed on the scene images from the different perspectives described above; Based on the features of images from different perspectives, feature consistency matching and feature completion are performed to obtain global image features; Based on the correlation analysis between the global features of the image and the actual physical space, a coordinate mapping is generated to obtain the 3D coordinates of each pixel; Based on the 3D coordinates of each pixel, the first position information of each camera device in the actual physical space is determined.

3. The camera equipment deployment method according to claim 2, characterized in that, The image global features are correlated with the actual physical space to generate a coordinate mapping, including: Based on the coordinate mapping of the actual physical space of the camera equipment in the deployment scheme design drawing, a third location information is generated; If the third location information is inconsistent with the second location information, then the second location information is updated with the third location information.

4. The camera equipment deployment method according to claim 1, characterized in that, The method further includes: Obtain the deployment parameters of each virtual node in the design and deployment scheme diagram, and send the deployment parameters to the corresponding camera device based on the binding relationship between the camera device and the virtual identifier; The deployment parameters include the location of the virtual nodes and the parameters of the camera equipment.

5. The camera equipment deployment method according to claim 1, characterized in that, The method further includes: Obtain the virtual viewpoint corresponding to each virtual node, as well as the actual viewpoint currently captured by each camera device; The actual viewpoint of the camera device corresponding to the matching relationship is compared with the virtual viewpoint of the virtual node to determine whether the two match. If there is no match, the matching relationship between the camera device deployment and the virtual node is re-established. Based on the updated matching relationship, the camera device is bound to a corresponding virtual identifier to obtain the updated deployment parameters. Send the updated deployment parameters to the corresponding camera devices.

6. The camera equipment deployment method according to claim 5, characterized in that, If the actual viewpoint of the camera device still cannot match the corresponding virtual viewpoint after the deployment parameters of the camera device are updated, an error message indicating that the viewpoint of the camera device has failed to match will be generated. The error message includes at least one of the following: the virtual identifier currently bound to the camera device, the actual deployment coordinates, and the viewing angle deviation data.

7. The method for deploying camera equipment according to any one of claims 4 to 5, characterized in that, The method further includes: The deployment parameters of each virtual node, the actual viewing angle of each camera device, and the corresponding virtual viewing angle are obtained and sent to the display module. The display module receives and displays the deployment parameters, actual viewpoint, and virtual viewpoint. At the same time, it establishes an association mapping between the deployment parameters and the viewpoint in the display interface, and presents the correspondence between the deployment parameters and the viewpoint in a visual manner based on the association mapping.

8. A camera equipment deployment device, characterized in that, The device includes: The image acquisition module is used to acquire scene images from different perspectives from multiple camera devices within the deployment area; The first processing module is used to perform content comparison on the images of each scene, and perform spatial correlation analysis based on the comparison results to determine the first position information of each camera device in the actual physical space. The second processing module is used to obtain a deployment scheme design diagram, which contains virtual nodes of multiple camera devices. Each virtual node has corresponding second location information and a unique virtual identifier. The third processing module is used to bind each camera device to a corresponding virtual identifier based on the matching relationship between its first location information and a corresponding second location information; wherein the matching relationship is determined at least based on the spatial similarity between the first location information and the second location information.

9. The camera equipment deployment device according to claim 8, characterized in that, The first processing module is specifically used to extract features from scene images captured by each camera device from different perspectives; perform feature consistency matching and feature completion based on the features of images from different perspectives to obtain global image features; and obtain global image features. Based on the correlation analysis between the global features of the image and the actual physical space, a coordinate mapping is generated to obtain the 3D coordinates of each pixel; based on the 3D coordinates of each pixel, the first position information of each camera device in the actual physical space is determined.

10. A computer-readable storage medium, characterized in that, It stores computer programs or instructions, which, when executed by a processor, implement the camera device deployment method.

Images