Method and device for automatically arranging video monitoring cameras and medium

Abstract

The invention discloses a method and equipment for automatically arranging video monitoring cameras, and a medium, and relates to the technical field of camera arrangement. The method comprises the following steps: receiving a user project, identifying a space type and an architectural drawing of the project, and configuring data; the space type and the camera type are mapped, a blind area tolerance threshold value and a coverage overlapping rate target are set, and the space type comprises a closed space and a lane area; the architectural drawing and the space type are analyzed, and based on the configuration data, camera arrangement points and orientations are obtained through calculation; the camera arrangement points and orientations of the closed space and the lane area and the coverage range and the blind area of the cameras are displayed on a graphical interface in an overlapped mode; and in response to dragging, deleting or newly adding operation of the user on the camera, updating the coverage range and the blind area in real time. According to the method, the arrangement scheme is rapidly generated, the number of the cameras can be intelligently optimized on the premise that the monitoring requirement is met, and the engineering cost is reduced.

Description

Technical Field

[0001] This application relates to the field of camera deployment technology, and in particular to a method, device and medium for automatic deployment of video surveillance cameras. Background Technology

[0002] In the design process of a video surveillance system, the placement and number of cameras directly determine the system's monitoring coverage and cost. Currently, the industry commonly uses 2D or 3D design software for manual placement. This method heavily relies on the designer's personal experience and subjective judgment, completing the initial design by manually placing camera icons one by one on drawings.

[0003] However, this design approach, which relies on human experience, has significant drawbacks. First, it is inefficient and difficult to guarantee rationality, as designers cannot quickly obtain quantitative coverage data for different layout schemes, leading to inaccurate assessments of monitoring blind spots. Second, when architectural floor plans are modified, designers must manually re-examine and adjust the placement of all relevant cameras, making it impossible to achieve rapid synchronous updates and adaptive optimization of the design. The entire design process is repetitive, cumbersome, and inconsistent.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows: Existing methods for deploying surveillance cameras rely on manual experience, resulting in low efficiency, difficulty in quantifying the rationality of deployment, and a lack of adaptive design. Summary of the Invention

[0005] This application provides a method, device, and medium for automatic deployment of video surveillance cameras, which can solve the problems of existing video surveillance camera deployment methods relying on manual experience, resulting in low efficiency, inability to quantify the rationality of deployment, and inability to adaptively respond to design.

[0006] In a first aspect, embodiments of this application provide a method for automatically deploying video surveillance cameras, characterized in that the method includes: receiving a user project, identifying the spatial type and architectural plan of the project, and configuration data; mapping the spatial type to the camera type, and setting a blind spot tolerance threshold and a coverage overlap rate target, wherein the spatial type includes enclosed spaces and driveway areas; analyzing the architectural plan and spatial type, and calculating the camera deployment points and orientations based on the configuration data; overlaying and displaying the camera deployment points and orientations of the enclosed spaces and driveway areas, as well as the camera coverage range and blind spots, on a graphical interface; and updating the coverage range and blind spots in real time in response to user drag-and-drop, deletion, or addition operations of cameras.

[0007] In one implementation of this application, the building plan is analyzed, and the camera placement points and orientations are calculated based on the configuration data. Specifically, this includes: obtaining the polygonal outline of the enclosed space and extracting all interior corner vertices; drawing rays from each interior corner vertex to the center point of the remaining interior corner vertices and the space entrance / exit, and using the set of ray directions as candidate monitoring directions; for each interior corner vertex and its corresponding candidate monitoring direction, taking the candidate monitoring direction as the central axis and the horizontal field of view of the configured camera type as the subtended angle, to obtain a fan-shaped area; calculating the visible area of ​​the part where the fan-shaped area intersects with the polygonal outline; and obtaining the candidate monitoring direction with the largest visible area as the optimal monitoring orientation.

[0008] In one implementation of this application, the method further includes: calculating the reward weight of each inner corner vertex covering the entrance / exit under the optimal monitoring orientation; combining the visible area with the reward weight to calculate the comprehensive layout priority for each inner corner vertex; iteratively selecting inner corner vertices to place cameras according to the comprehensive layout priority; and updating the uncovered area of ​​the space after each iteration.

[0009] In one implementation of this application, the reward weight for each inner corner vertex covering the entrance / exit under the optimal monitoring orientation is calculated. Specifically, this includes: determining whether the fan-shaped area emanating from the optimal monitoring orientation of the inner corner vertex covers the center point of at least one entrance / exit; if it does, then an additional priority boost is assigned to the overall layout priority of the inner corner vertex, and the priority boost is positively correlated with the visible area.

[0010] In one implementation of this application, the building plan is analyzed, and the camera placement points and orientations are calculated based on the configuration data. Specifically, this includes: expanding symmetrically outwards to both sides based on the lane centerline to construct a lane monitoring area; using the endpoints of the lane centerline, as well as the intersections of the lane centerline with the fire compartment boundary line, wall line, and parking space line, respectively, as a pre-selected set of camera placement points; and, based on the pre-selected set of points, sequentially placing cameras along the lane centerline direction according to the effective monitoring length of the cameras.

[0011] In one implementation of this application, the method further includes: after each deployment, detecting blind spot segments in the remaining uncovered lane area; if the length of the blind spot segment exceeds the blind spot tolerance threshold, calculating and inserting new camera deployment points within the blind spot segment; and offsetting pre-selected points located at lane intersections and fire compartment boundaries by a preset distance along the lane centerline direction.

[0012] In one implementation of this application, in response to a user's dragging, deleting, or adding operation on the camera location, the coverage area and blind spot display are updated in real time. Specifically, this includes: on the graphical interface, filling the enclosed space and lane area where the camera is located with a first identification color, filling the area already covered by the camera with a semi-transparent second identification color, and filling the identified blind spot with a third identification color; in response to a user's dragging operation on the camera, calculating the coverage area of ​​the camera at the new location, and updating the coverage area and blind spot; in response to a user's deletion or addition of a camera, updating the camera list and coverage analysis results.

[0013] In one implementation of this application, after identifying the building floor plan and calculating the camera placement points and orientations based on configuration data, and the building floor plan includes enclosed spaces and driveway areas, the method further includes: generating multiple different camera placement schemes by adjusting the blind spot tolerance threshold or coverage overlap rate target; quantifying and comparing the total number of cameras, total coverage area, and maximum blind spot size in the camera placement schemes, and displaying the results.

[0014] Secondly, embodiments of this application also provide a device for automatically deploying video surveillance cameras. The device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed, enable the at least one processor to: receive a user project, identify the spatial type and architectural plan of the project, and configuration data; map the spatial type to the camera type, and set a blind spot tolerance threshold and a coverage overlap rate target, wherein the spatial type includes enclosed spaces and lane areas; analyze the architectural plan and spatial type, and calculate the camera deployment points and orientations based on the configuration data; overlay and display the camera deployment points and orientations of enclosed spaces and lane areas, as well as the camera coverage and blind spots, on a graphical interface; and update the coverage and blind spots in real time in response to user dragging, deleting, or adding operations on cameras.

[0015] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for automatic deployment of video surveillance cameras, storing computer-executable instructions. These instructions are configured to: receive a user project; identify the spatial type and architectural plan of the project, as well as configuration data; map the spatial type to the camera type, and set a blind spot tolerance threshold and a coverage overlap rate target, where the spatial type includes enclosed spaces and driveway areas; analyze the architectural plan and spatial type, and calculate the camera deployment points and orientations based on the configuration data; overlay and display the camera deployment points and orientations of enclosed spaces and driveway areas, as well as the camera coverage and blind spots, on a graphical interface; and update the coverage and blind spots in real time in response to user drag-and-drop, deletion, or addition operations of cameras.

[0016] This application provides a method, device, and medium for the automatic deployment of video surveillance cameras. It replaces repetitive manual operations with automated algorithms, quickly generating deployment plans and intelligently optimizing the number of cameras while meeting monitoring requirements, thus reducing project costs. Deployment based on unified configuration rules and algorithms avoids inconsistencies in design outcomes caused by differences in individual designer experience. Furthermore, it uses quantitative data such as coverage area and blind spot size for plan comparison and decision-making, making the design results more scientific and accurate. When architectural floor plans are modified, it automatically identifies the changed areas and incrementally updates the deployment plan, greatly improving modification efficiency. Simultaneously, the visual interface and real-time interactive functions allow designers to flexibly fine-tune the automatically generated plans, forming a highly efficient workflow that combines automatic generation with manual optimization. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a method for automatically deploying video surveillance cameras, as provided in this application embodiment; Figure 2 A schematic diagram of the interior corner vertices of a method for automatically deploying video surveillance cameras provided in an embodiment of this application; Figure 3 A schematic diagram of candidate orientations for a method of automatically deploying video surveillance cameras provided in an embodiment of this application; Figure 4 A schematic diagram of the visible area of ​​a method for automatically deploying video surveillance cameras provided in an embodiment of this application; Figure 5 A visual interface diagram illustrating a method for automatically deploying video surveillance cameras provided in an embodiment of this application; Figure 6 This is a schematic diagram of the internal structure of a device for automatically deploying video surveillance cameras, provided as an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. 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.

[0019] This application provides a method, device, and medium for automatic deployment of video surveillance cameras, which solves the problems of existing video surveillance camera deployment methods relying on manual experience, resulting in low efficiency, inability to quantify the rationality of deployment, and inability to adaptively respond to design.

[0020] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0021] Figure 1 This is a flowchart illustrating a method for automatically deploying video surveillance cameras, as provided in an embodiment of this application. Figure 1 As shown in the figure, an embodiment of this application provides a method for automatically deploying video surveillance cameras, which specifically includes the following steps: Step 10: Receive user projects, identify the spatial type and architectural drawings of the projects, as well as configuration data.

[0022] In this step, the system receives the user-specified design project, including the building floor plan to be monitored. It then parses the layer information, geometric features, and attribute information of the floor plan, identifies various spaces in the drawing, and classifies them into different space types according to preset rules, including enclosed spaces such as rooms, anterooms, elevator lobbies, corridors, and driveway areas. Simultaneously, it retrieves or receives configuration data input by the user, which can originate from pre-stored national / industry standards or user-defined enterprise standards.

[0023] Step 20: Map space types to camera types and set blind spot tolerance thresholds and coverage overlap targets. Space types include enclosed spaces and lane areas.

[0024] In this step, a mapping relationship is established between the identified space type and the specific camera type. For example, the anteroom space type is mapped to a dome camera, and the corridor space type is mapped to a bullet camera. This mapping ensures that areas with different functions can automatically match the most suitable monitoring equipment.

[0025] In addition, the system also loads key deployment target parameters from the configuration data: Blind spot tolerance threshold: defines the maximum size of continuous blind spots that the algorithm can accept without coverage, used to balance coverage integrity and the number of devices. For example, in lane areas, monitoring blind spots shorter than this threshold are allowed to avoid over-deploying cameras; Coverage overlap rate target: defines the expected field of view overlap ratio at key area entrances and exits or at the intersection of multiple camera fields of view, which helps to ensure the continuity of monitoring and the effectiveness of target tracking.

[0026] Table 1 Monitoring Types

[0027] Step 30: Analyze the building plans and space types, and calculate the camera placement points and orientations based on the configuration data; As an optional embodiment, the analysis of architectural drawings and space types, based on configuration data, calculates the camera placement points and orientations, which may specifically include: Step 301: Obtain the polygonal outline of the enclosed space and extract all interior corner vertices; Step 302: Draw rays from each interior corner vertex to the center point of the remaining interior corner vertices and the space entrance / exit, and use the set of ray directions as candidate monitoring directions; Step 303: For each interior corner vertex and the corresponding candidate monitoring direction, with the candidate monitoring direction as the central axis and the horizontal field of view of the configured camera type as the subtended angle, obtain a fan-shaped area; Step 304: Calculate the visible area of ​​the part where the fan-shaped area intersects with the polygonal outline; Step 305: Obtain the candidate monitoring direction with the largest visible area as the optimal monitoring orientation.

[0028] In this step, the space is first discretized to obtain the outline of the interior wall of the room where the surveillance cameras will be installed. Then, the interior corner points P, P1, P2, P3...Pi of the polygonal outline and the center point of the entrance / exit are obtained, such as... Figure 2 and Figure 3 As shown, lines of sight are drawn from each vertex to other visible vertices and the center point of the exit; these lines of sight are used as candidate directions for each vertex; the visible area A within the ±FOV (Horizontal Field of View) sector is calculated for each candidate direction θi of each vertex; two rays are emitted from P in the direction θ±FOV / 2; all visible points between these two rays constitute a visible sector region; as shown... Figure 4 As shown, the actual visible area Ai = the visible part of the sector inside the polygon. The direction with the largest area is selected as the best orientation for the vertex.

[0029] As an optional embodiment, the method may further include: Step 306: Calculate the reward weight of each inner corner vertex covering the entrance / exit under the optimal monitoring orientation, combine the visible area with the reward weight, and calculate the comprehensive layout priority for each inner corner vertex; Step 307: Iteratively select inner corner vertices to place cameras according to the comprehensive layout priority, and update the uncovered area of ​​the space after each iteration.

[0030] In this step, for each vertex Pi, a scoring function is defined with the goal of increasing the exit priority weight: S(Pi) = Ai + λ × I, where: Ai is the visible area of ​​the vertex under the optimal orientation; I: 1 if the camera can see the exit, 0 otherwise; λ = 0.2 × (Ai), the camera position is selected using a greedy algorithm, initialized as follows: uncovered area = the entire polygon, selected camera set = empty. Loop: Among all unselected vertices, select the vertex Pi with the largest S(Pi); End: Complete the comparison of all vertices.

[0031] As an optional embodiment, the reward weight for each inner corner vertex covering the entrance / exit under the optimal monitoring orientation can be calculated. Specifically, it can include: Step 3061: Determine whether the fan-shaped area emanating from the optimal monitoring orientation of the inner corner vertex covers the center point of at least one entrance / exit; Step 3062: If it covers, assign an additional priority boost to the overall layout priority of the inner corner vertex. The priority boost is positively correlated with the visible area.

[0032] As an optional embodiment, the building plan and space type are analyzed, and the camera placement points and orientations are calculated based on the configuration data. Specifically, this may include: Step 301': Based on the lane centerline, the lane monitoring area is symmetrically expanded outwards to both sides; Step 302': The endpoints of the lane centerline, as well as the intersections of the lane centerline with the fire compartment boundary line, wall line, and parking space line, are used as a pre-selected set of camera placement points; Step 303': According to the pre-selected set of points, cameras are sequentially placed along the lane centerline direction based on the effective monitoring length of the cameras.

[0033] In this step, lane vector data, i.e., the lane centerline, is acquired. This data consists of a series of coordinates. Straight segments are selected, and their endpoints are extended until they intersect with other lane lines, fire compartment outlines, wall lines, or parking space lines. Using all lane lines as the center, the data is expanded outwards by 2750mm to both sides, independently enclosing each fire compartment as a lane area S. A rectangular visible area of ​​length C and width 5500mm is drawn with each camera facing the same direction. The endpoints of the lane lines, the intersections of the lane lines with the fire compartment outline, the intersections of the lane lines with the wall lines, and the intersections of the lane lines with the parking space lines serve as a list of possible pre-selected camera points.

[0034] As an optional embodiment, the method may further include: step 304': after each deployment, detect the blind spot segments in the remaining uncovered lane area; step 305': if the length of the blind spot segment exceeds the blind spot tolerance threshold, calculate and insert a new camera deployment point within the blind spot segment; step 306': for the pre-selected points located at lane intersections and fire compartment boundaries, offset by a preset distance along the lane centerline direction.

[0035] In this step, cameras are placed sequentially according to the priority of each point in the previous step, and the blind spot length is checked. The length L of the lane line where the blind spot does not meet the requirements is recorded. The number of cameras to be added in the middle is calculated as N=L / C, where C is the effective monitoring distance. When the remainder L / C is less than the blind spot tolerance M in the user variable system module, N is rounded up to the nearest integer; otherwise, it is rounded down. Cameras are placed at the calculated points, and the blind spot length is checked again. The process stops when the blind spot length is less than or equal to M.

[0036] Furthermore, the actual position of the camera is finely adjusted. At the intersection of the lane line and the fire compartment outline, the camera is offset by 1200mm along the direction of the lane line to avoid the roller shutter at the fire compartment. At the intersection of the lane lines, the camera is offset by 2750mm in the opposite direction of the lane, which is half the width of the lane, in order to avoid placing it in the exact center of the intersection.

[0037] Step 40: Overlay the camera placement and orientation in the enclosed space and lane area, as well as the camera coverage and blind spots, onto the graphical interface.

[0038] In this step, such as Figure 5 As shown, the system will visually overlay the automated layout results onto the original building floor plan as a graphical layer. Each layout point will be precisely displayed as an icon, with the icon style reflecting the camera type and a directional indicator or fan-shaped area clearly indicating the camera's monitoring orientation. The effective monitoring area of ​​each camera at its specific location and orientation, calculated based on its monitoring radius and horizontal field of view, will be graphically plotted. By comparing the sum of the coverage areas of all cameras with the area to be monitored, the system automatically calculates the uncovered areas and marks them as blind spots.

[0039] Step 50: Respond to user actions such as dragging, deleting, or adding cameras, and update the coverage area and blind spots in real time.

[0040] As an optional embodiment, in response to user dragging, deleting, or adding camera locations, the coverage area and blind spot display are updated in real time. Specifically, this may include: Step 501: On the graphical interface, the enclosed space and lane area where the camera is located are filled with a first identification color, the area already covered by the camera is filled with a semi-transparent second identification color, and the identified blind spots are filled with a third identification color; Step 502: In response to user dragging of the camera, the coverage area of ​​the camera at the new location is calculated, and the coverage area and blind spots are updated; Step 503: In response to user deletion or addition of a camera, the camera list and coverage analysis results are updated.

[0041] In this step, the new coordinates of the camera are captured. Based on the camera type parameters, the monitoring coverage area under the new location and orientation is recalculated. The orientation can be preset to follow the drag direction or remain unchanged. Based on this new local coverage area, combined with the coverage areas of all other cameras, the global coverage area and blind spot distribution of the entire project are recalculated and updated. On the graphical interface, the fill range of the second and third identification colors is updated in real time.

[0042] As an optional embodiment, after identifying the building floor plan and calculating the camera placement points and orientations based on configuration data, and the building floor plan includes enclosed spaces and driveway areas, the method may further include: generating multiple different camera placement schemes by adjusting the blind spot tolerance threshold or coverage overlap rate target; quantifying and comparing the total number of cameras, total coverage area, and maximum blind spot size in the camera placement schemes, and displaying the results.

[0043] In this step, user instructions for adjusting key deployment parameters are received, including at least the blind zone tolerance threshold and coverage overlap rate target. Users can easily adjust the values ​​of these parameters through interactive elements such as sliders and input boxes on the graphical interface. For example, users can adjust the blind zone tolerance threshold from a lower value to a higher value to allow for larger monitoring blind zones; or increase the coverage overlap rate target from a lower level to a higher level to require more stringent monitoring coverage.

[0044] Subsequently, based on each different combination of parameters, the system automatically re-executes the camera placement calculation process. Each calculation generates a complete and feasible camera placement plan. In this way, the system can quickly generate multiple alternative plans with different focuses in terms of monitoring coverage, number of devices, and economic cost. Next, the system performs parallel analysis and quantitative comparison of the generated placement plans. The system extracts the key performance indicators for each plan, which include at least: Total number of cameras: directly reflecting the cost of the plan; Total coverage area: reflecting the overall monitoring range of the plan.

[0045] Maximum blind spot size: The weakest link in the evaluation scheme. The above quantitative comparison results are presented to the user in a visual format. For example, the indicator data of each scheme can be clearly listed in a table in a side-by-side window, or radar charts and other charting tools can be used to intuitively display the advantages and disadvantages of different schemes in various dimensions. This data-driven presentation method allows users to go beyond simple experience-based judgment and select the best layout scheme that best meets the actual needs of the current project based on objective data. This enhances the adaptability and decision support capabilities of the method, elevating the originally simple automated output into a comprehensive intelligent design platform that supports multi-dimensional parameter adjustment, automatic generation of multiple schemes, and quantitative comparison.

[0046] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a device for automatically deploying video surveillance cameras, the structure of which is as follows: Figure 6 As shown.

[0047] Figure 6 This is a schematic diagram of the internal structure of a device for automatically deploying video surveillance cameras, provided as an embodiment of this application. Figure 6 As shown, the device includes: At least one processor 601; And a memory 602 that is communicatively connected to at least one processor; The memory 602 stores instructions executable by at least one processor 601, which enables the processor 601 to: receive a user project, identify the spatial type and architectural plan of the project, and configuration data; map the spatial type to the camera type, and set a blind spot tolerance threshold and coverage overlap target, wherein the spatial type includes enclosed spaces and driveway areas; analyze the architectural plan and spatial type, and calculate the camera placement points and orientations based on the configuration data; overlay and display the camera placement points and orientations of enclosed spaces and driveway areas, as well as the camera coverage and blind spots, on a graphical interface; and update the coverage and blind spots in real time in response to user drag-and-drop, deletion, or addition of cameras.

[0048] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for automatic deployment of video surveillance cameras is disclosed, storing computer-executable instructions. These instructions are configured to: receive a user project; identify the spatial type and architectural plan of the project, as well as configuration data; map the spatial type to the camera type and set a blind spot tolerance threshold and coverage overlap target; the spatial types include enclosed spaces and driveway areas; analyze the architectural plan and spatial type, and calculate the camera placement points and orientations based on the configuration data; overlay and display the camera placement points and orientations, as well as the camera coverage and blind spots, on a graphical interface for enclosed spaces and driveway areas; and update the coverage and blind spots in real time in response to user drag-and-drop, deletion, or addition operations of cameras.

[0049] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0050] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0051] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0055] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0056] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0057] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0058] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0059] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for automatic placement of video surveillance cameras, characterized in that, The method comprises: receiving user projects, identifying the spatial type and building plan of the projects, and configuration data; mapping the spatial type with camera types, and setting a blind area tolerance threshold and a coverage overlap rate target, the spatial type including enclosed spaces and driveway areas; analyzing the building plan and spatial type, and calculating camera arrangement points and orientations based on the configuration data; superimposedly displaying the camera arrangement points and orientations of the enclosed spaces and driveway areas, and the coverage range and blind area of the cameras on a graphical interface; in response to user drag, delete or add operations on the cameras, updating the coverage range and blind area in real time.

2. The method for automatic arrangement of video monitoring cameras according to claim 1, characterized in that, The analysis of the building plan and spatial type, and the calculation of camera arrangement points and orientations based on the configuration data, specifically comprises: obtaining the polygonal contour of the enclosed space, and extracting all internal corner vertices; at each internal corner vertex, a ray is drawn to the center point of the current remaining internal corner vertex and the space entrance and exit, and the direction set of the ray is taken as a candidate monitoring direction; for each internal corner vertex and the corresponding candidate monitoring direction, a sector area is obtained with the candidate monitoring direction as the central axis and the horizontal field of view angle of the configured camera type as the opening angle; calculating the visible area of the intersection part of the sector area and the polygonal contour; obtaining the candidate monitoring direction with the maximum visible area as the optimal monitoring orientation.

3. The method for automatic arrangement of video surveillance cameras according to claim 2, characterized in that, The method further comprises: calculating the reward weight of each internal corner vertex covering the entrance and exit in the optimal monitoring orientation, combining the visible area with the reward weight, and calculating the comprehensive arrangement priority of each internal corner vertex; according to the comprehensive arrangement priority, iteratively selecting the internal corner vertices to arrange cameras, and updating the uncovered area of the space after each iteration.

4. The method for automatic arrangement of video surveillance cameras according to claim 3, characterized in that, The calculation of the reward weight of each internal corner vertex covering the entrance and exit in the optimal monitoring orientation specifically comprises: judging whether the sector area emitted from the optimal monitoring orientation of the internal corner vertex covers at least one center point of the entrance and exit; if yes, an additional priority promotion amount is given to the comprehensive arrangement priority of the internal corner vertex, and the priority promotion amount is positively correlated with the visible area.

5. The method for automatic arrangement of video surveillance cameras according to claim 4, characterized in that, The analysis of the building plan and spatial type, and the calculation of camera arrangement points and orientations based on the configuration data, specifically comprises: based on the lane center line, symmetrically expanding to both sides to construct a lane monitoring area; taking the end points of the lane center line, and the intersection points of the lane center line with the fire compartment boundary line, wall line and parking space line, as a preselected point set for camera arrangement; according to the preselected point set, arranging cameras along the direction of the lane center line based on the effective monitoring length of the camera.

6. The method for automatic arrangement of video surveillance cameras according to claim 5, characterized in that, The method further comprises: after each arrangement, detecting a blind area line segment that does not cover the remaining driveway area; if the length of the blind area line segment exceeds the blind area tolerance threshold, calculating and inserting a new camera arrangement point in the blind area line segment; for the preselected points located at the lane intersection and the fire compartment boundary line, offsetting by a preset distance along the direction of the lane center line.

7. The method for automatic arrangement of video surveillance cameras according to claim 1, characterized in that, The method comprises the following steps: In the graphic interface, the closed space and the lane area where the camera exists are filled with a first identification color, the area covered by the camera is filled with a semi-transparent second identification color, and the identified blind area is filled with a third identification color; In response to the user's dragging operation on the camera, the coverage of the camera at the new position is calculated, and the coverage and the blind area are updated; In response to the user's operation of deleting or adding a camera, the camera list and the coverage analysis result are updated.

8. The method for automatic arrangement of video surveillance cameras according to claim 1, characterized in that, After the building plan is identified and the camera arrangement point and the orientation are calculated based on the configuration data, the method further comprises the following steps: By adjusting the blind area tolerance threshold or the coverage overlap rate target, a plurality of different camera arrangement schemes are generated; The total number of cameras, the total coverage area, and the maximum blind area size in the camera arrangement scheme are quantitatively compared and displayed.

9. An apparatus for automatic placement of video surveillance cameras, characterized in that, The device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: receive a user project, identify the space type and the building plan of the project, and configuration data; map the space type to the camera type, and set the blind area tolerance threshold and the coverage overlap rate target, the space type including a closed space and a lane area; analyze the building plan and the space type, and calculate the camera arrangement point and the orientation based on the configuration data; superimpose and display the camera arrangement result of the closed space and the lane area, the coverage of the camera, and the blind area on the graphic interface; in response to the user's dragging, deleting, or adding operation on the camera position, update the coverage and the blind area in real time. 10.A non-transitory computer storage medium storing computer-executable instructions for video surveillance camera automatic arrangement, the computer-executable instructions comprising: The computer executable instructions are configured to: receive a user project, identify the space type and the building plan of the project, and configuration data; map the space type to the camera type, and set the blind area tolerance threshold and the coverage overlap rate target, the space type including a closed space and a lane area; analyze the building plan and the space type, and calculate the camera arrangement point and the orientation based on the configuration data; superimpose and display the camera arrangement result of the closed space and the lane area, the coverage of the camera, and the blind area on the graphic interface; in response to the user's dragging, deleting, or adding operation on the camera position, update the coverage and the blind area in real time.

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