Monitoring device positioning method, apparatus and system

Abstract

The application discloses a kind of monitoring equipment positioning method, device and system, and the present application is first to each monitoring equipment in the video data of monitoring that mark moves along each monitoring equipment and is located space, extract the video frame and corresponding monitoring equipment that mark appears in the monitoring video data, and the visible surface of mark is identified from video frame;Second, according to the pose data of mark along each monitoring equipment in the process of moving in the space where each monitoring equipment is located and the visible surface of the mark identified, a segmentation line is constructed;Finally, the space of each monitoring equipment is recursively segmented by segmentation line, and geographical environment constraint is added, and the spatial position and azimuth angle of each monitoring equipment are estimated.The present application can realize monitoring equipment positioning.

Description

Technical Field

[0001] This invention relates to the field of computers, and more particularly to a method, apparatus and system for locating monitoring equipment. Background Technology

[0002] Surveillance equipment is ubiquitous in public areas and various important locations. The rapid increase in the number of surveillance devices has generated massive amounts of surveillance video data. For the entire surveillance system, the images on the monitors are disordered. Monitoring personnel often need to understand the location of the camera corresponding to each part of the image on the monitor in order to effectively conduct security monitoring. Most existing surveillance equipment lacks location references, making it impossible to establish a location mapping relationship between surveillance equipment and the surveillance network, and impossible to achieve virtual registration of surveillance equipment in geographic space. Camera management and maintenance in the surveillance system are difficult, and existing surveillance equipment resources cannot be fully utilized. Summary of the Invention

[0003] Purpose of the invention: This invention addresses the problems existing in the prior art by providing a method, apparatus, and system for locating monitoring equipment that can provide location mapping between monitoring devices and monitoring networks.

[0004] Technical solution: This invention provides a method for locating monitoring equipment, comprising the following steps:

[0005] (1) Acquire the monitoring video data captured by each monitoring device when the marker moves along the space where each monitoring device is located;

[0006] (2) Acquire the pose data of the marker as it moves along the space where each monitoring device is located;

[0007] (3) Extract the video frames containing the markers and the corresponding monitoring devices from the monitoring video data, and identify the visible faces of the markers from the video frames;

[0008] (4) Construct a dividing line by combining the visible face and pose data of the identified landmarks;

[0009] (5) Recursively divide the space of each monitoring device by dividing the dividing line, and add geographical environment constraints to estimate the spatial location of each monitoring device;

[0010] (6) Estimate the azimuth angle of each monitoring device based on the spatial location of each monitoring device and the start and end pose data of the marker appearing within the monitoring device range.

[0011] Furthermore, the surveillance video data includes a sequence of surveillance video frames and a timestamp for each surveillance video frame.

[0012] Furthermore, step (2) specifically includes:

[0013] (2-1) Obtain the positioning data of the marker as it moves along the space where each monitoring device is located;

[0014] (2-2) Parse the positioning data to obtain the position of the marker at each collection moment during the movement of the marker along the space where each monitoring device is located;

[0015] (2-3) Based on the positioning data and the position of the marker at each acquisition time, calculate the azimuth angle of the marker at each acquisition time, and use the position and azimuth angle of the marker as the pose data of the marker.

[0016] Furthermore, step (3) includes:

[0017] (3-1) Obtain the monitoring device and timestamp corresponding to each video frame in the monitoring video data;

[0018] (3-2) Based on the position data acquisition time of the marker, obtain video frames with the same time stamp;

[0019] (3-3) Convert the acquired video frames to the HSV color space;

[0020] (3-4) For video frames in the HSV color gamut space, the contour recognition method is used to identify whether there are markers, and video frames with markers are retained.

[0021] (3-5) Based on the color information of the markers in the retained video frames, identify the visible faces of the markers in the video frames, wherein each face of the marker has a different color.

[0022] Furthermore, step (4) includes:

[0023] (4-1) Obtain the marker pose information corresponding to the video frame in which the marker appears from the marker pose data. Specifically, the marker pose information corresponding to the video frame is the marker pose information whose time is consistent with the timestamp of the video frame.

[0024] (4-2) Based on the visible faces of the landmarks in each video frame and the corresponding landmark pose information, construct the segmentation line according to the following formula:

[0025] y - y0 = k(x - x0)

[0026] Where x and y represent variables in the segmentation line equation, (x0, y0) represents the position coordinates in the marker pose information, and k is the azimuth parameter. If the visible side of the marker in the video frame is only the side, k is the tangent of the azimuth angle of the device's forward direction. If only the front is visible, then k is the tangent of the azimuth angle of the segmentation line generated on the front side of the device at a 90-degree angle to the forward direction. If both the front and side are visible, then two segmentation lines are generated simultaneously.

[0027] Furthermore, step (5) includes:

[0028] (5-1) Determine the initial space of the monitoring equipment based on prior knowledge of the monitoring equipment;

[0029] (5-2) For a dividing line, process it as follows: use the dividing line to perform binary space division on the initial space, and according to the direction of the dividing line, retain the half space where the monitoring equipment is located in the binary space division result as the initial space for the next dividing line.

[0030] (5-3) For the space remaining after all the dividing lines have been completed, add the building outlines and object outlines in the space as geographical environment constraints to obtain an irregular polygon.

[0031] (5-4) Obtain the centroid of the irregular polygon as the spatial location of the monitoring device.

[0032] Furthermore, step (6) includes:

[0033] (6-1) Obtain the starting point of the marker when it first appears in the range of the monitoring equipment, and the ending point when it leaves the range of the monitoring equipment from the pose data;

[0034] (6-2) Based on the spatial location of the monitoring equipment and the data obtained in step (6-1), calculate the azimuth angle of the monitoring equipment using the following formula:

[0035]

[0036] In the formula, (x s ,y s ) represents the spatial location of the monitoring equipment, set as point O, α OP Let α be the azimuth angle of the monitoring equipment, (x1, y1) represent the starting point (point A), (x2, y2) represent the ending point (point B), and α be the azimuth angle of the monitoring equipment. OA α represents the azimuth angle between the monitoring equipment and the starting point. OB This indicates the azimuth angle between the monitoring equipment and the termination point.

[0037] Furthermore, the method for calculating the azimuth angle of the marker at each acquisition time is as follows:

[0038]

[0039]

[0040] In the formula, α and β represent the heading angle and pitch angle of the marker, respectively, and ΔE, ΔN, and ΔH are the coordinate differences between the primary and secondary positioning sensors in the northeast-central coordinate system at the moment of marker acquisition.

[0041] The present invention also provides a monitoring equipment positioning device, comprising:

[0042] The first data acquisition module is used to acquire monitoring video data captured by each monitoring device when the marker moves along the space where each monitoring device is located;

[0043] The second data acquisition module is used to acquire the pose data of the marker as it moves along the space where each monitoring device is located;

[0044] The identification module is used to query the video frames in the monitoring video data in which the marker appears and the corresponding monitoring devices, and to identify the visible faces of the marker from the video frames;

[0045] The segmentation line construction module is used to construct segmentation lines by combining the visible face and pose data of the identified landmarks;

[0046] The location estimation module is used to recursively divide the space of each monitoring device by the dividing line, add geographical constraints, and estimate the spatial location of each monitoring device.

[0047] The azimuth estimation module is used to estimate the azimuth of each monitoring device based on the spatial location of each monitoring device and the start and end positions of the markers within the monitoring device's range.

[0048] The present invention also provides a monitoring equipment positioning system, comprising:

[0049] A marker, wherein a positioning sensor is installed inside the marker;

[0050] A carrier for transporting the markers along the spatial arrangement of each monitoring device;

[0051] Surveillance equipment, used to capture video data of the movement of markers; and

[0052] The aforementioned monitoring equipment positioning device.

[0053] Beneficial effects: Compared with the prior art, the significant advantages of this invention are: This invention obtains the spatial position and attitude information of the marker by the movement of the marker in the outdoor space, and estimates the spatial position and orientation of the camera by the monitoring image containing the marker captured by the monitoring equipment at the same time. This makes the estimation of the spatial position and azimuth of the outdoor monitoring equipment faster, more efficient and less costly, and provides a new approach for the investigation of the current deployment status of outdoor monitoring cameras and the optimization of spatial layout. Attached Figure Description

[0054] Figure 1 This is a flowchart of the monitoring equipment positioning method provided by the present invention;

[0055] Figure 2 This is a schematic diagram of the initial space where the monitoring equipment is located;

[0056] Figure 3 This is a schematic diagram of generating dividing lines (surfaces) based on visibility;

[0057] Figure 4 This is a schematic diagram showing the division of the space where the monitoring equipment is located;

[0058] Figure 5 This is a schematic diagram illustrating the spatial location estimation of monitoring equipment under geographical constraints.

[0059] Figure 6 This is a schematic diagram of the orientation angle estimation for monitoring equipment;

[0060] Figure 7 This is a structural diagram of the monitoring equipment positioning device provided by the present invention;

[0061] Figure 8 This is a schematic diagram of the monitoring equipment positioning system provided by the present invention;

[0062] Figure 9 This is a distribution map of surveillance cameras in the experimental area;

[0063] Figure 10 This is a schematic diagram comparing the positioning results of the present invention with the actual location. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Example 1

[0066] This embodiment provides a method for locating monitoring equipment, such as... Figure 1 As shown, it includes the following steps:

[0067] S1. Acquire monitoring video data captured by each monitoring device as the marker moves along the space where each monitoring device is located. Before positioning, a marker is set up. To better identify the marker's faces, it is set as a cuboid with each face being a different color. During movement, the marker moves in a uniform straight line along the space where each monitoring device is located. After stable position and attitude data are generated, the marker moves freely at a slower speed to obtain more accurate marker movement trajectory and attitude information. Simultaneously, when the marker enters its nearby monitoring area during movement, the monitoring devices can obtain a clearer image of the marker. After the marker has finished moving, the monitoring video captured during its movement is retrieved from the monitoring system. The monitoring device number and start timestamp corresponding to the monitoring video are obtained. The monitoring video is processed into a sequence of monitoring video frames, and the timestamp of each monitoring video frame is calculated as the monitoring video data.

[0068] S2. Acquire the pose data of the marker as it moves along the space where each monitoring device is located.

[0069] This step specifically includes:

[0070] S21. Obtain the positioning data of the marker as it moves along the space where each monitoring device is located.

[0071] The markers are equipped with positioning sensors, such as GNSS sensors, which perform real-time positioning according to the collection frequency during movement, that is, they collect positioning data once every certain period of time.

[0072] S22. Analyze the positioning data to obtain the position of the marker at each collection moment during the movement of the marker along the space where each monitoring device is located.

[0073] The original positioning data from the positioning sensor is in NEMA-0183 format. Parsing this message allows us to obtain the latitude, longitude, and elevation data of the sensor antenna phase center at any given time, and then calculate the position coordinates in the local coordinate system. The calculation process is as follows:

[0074] First, convert the positioning data from the WGS84 coordinate system to the ECEF (Geocentric-Earth-Fixed) coordinate system, as shown in the following expression:

[0075] x′=(N+H)*cos(B)*cos(L)

[0076] y′=(N+H)*cos(B)*sin(L)

[0077] z′=(N*(1-e*e)+H)*sin(B)

[0078] Where N is the radius of curvature of the reference ellipsoid, B, L, and H represent latitude, longitude, and elevation, respectively, and x′, y′, and z′ represent the coordinates in the transformed geocentric coordinate system.

[0079] The geocentric coordinate system is then transformed into the local coordinate system, as shown in the following expression:

[0080]

[0081] Where B0 and l0 are the latitude and longitude coordinates of the origin of the local coordinate system, (Δ x ,Δ y ,Δ z (x, y, z) represents the difference between the current coordinates of the marker in the geocentric geofixed coordinate system and the coordinates of the local coordinate system origin, and (x, y, z) represents the final position coordinates.

[0082] S23. Based on the positioning data and the position of the marker at each acquisition time, calculate the azimuth angle of the marker at each acquisition time, and use the position and azimuth angle of the marker as the pose data of the marker.

[0083] The method for calculating the azimuth angle is as follows:

[0084]

[0085]

[0086] In the formula, α and β represent the heading angle and pitch angle of the marker, respectively, and ΔE, ΔN, and ΔH are the coordinate differences between the primary and secondary positioning sensors in the northeast-central coordinate system at the moment of marker acquisition.

[0087] S3. Extract the video frames containing the markers and the corresponding monitoring devices from the monitoring video data, and identify the visible faces of the markers from the video frames.

[0088] This step specifically includes:

[0089] S31. Obtain the monitoring device and timestamp corresponding to each video frame in the monitoring video data.

[0090] S32. Based on the position data of the marker and the data acquisition time, obtain video frames with the same timestamp.

[0091] S33. Convert the acquired video frames to the HSV color gamut space.

[0092] Based on the video frames obtained in the previous step, the original color gamut space is RGB. To facilitate computer recognition, it needs to be converted to HSV color gamut space for analysis. During the conversion, the colors of the three RGB channels need to be normalized, and then the H, S, and V channels are calculated separately, and the brightness V is histogram equalized to reduce the interference of light intensity factors on the recognition results.

[0093] When calculating H, it is necessary to determine the magnitudes of the normalized R, G, and B components, as shown in the following formula:

[0094]

[0095] The expression for finding s is:

[0096]

[0097] In the formula, r, g, b represent the R, G, and B components, respectively, and h, s represent the H and S components, respectively.

[0098] S34. For video frames in the HSV color gamut space, use contour recognition to identify whether there are markers and retain the video frames that have markers.

[0099] Contour recognition for object identification is an existing technology and will not be discussed further here.

[0100] S35. Based on the color information of the markers in the retained video frames, identify the visible faces of the markers in the video frames, wherein each face of the marker has a different color.

[0101] Each side of the marker has a different color. By observing the color of the marker in the video frame in the HSV color gamut, we can determine which side of the marker is visible in the video frame, thus obtaining the visible side.

[0102] S4. Combine the visible surfaces and pose data of the identified landmarks to construct a dividing line.

[0103] This step specifically includes:

[0104] S41. Obtain the pose information of the marker corresponding to the video frame in which the marker appears from the pose data of the marker.

[0105] Specifically, the marker pose information corresponding to the video frame is the marker pose information whose time is consistent with the video frame timestamp, including position coordinates and azimuth angle.

[0106] S42. Based on the visible faces of the landmarks in each video frame and the corresponding landmark pose information, construct the dividing line according to the following formula:

[0107] y - y0 = k(x - x0)

[0108] Where x and y represent variables in the segmentation line equation, (x0, y0) represents the position coordinates in the marker's pose information, and k is the azimuth parameter. If the visible side of the marker in the video frame is only the side, k is the tangent of the azimuth angle of the device's forward direction. If only the front is visible, k is the tangent of the azimuth angle of the segmentation line generated on the front side of the device at a 90-degree angle to the forward direction. If both the front and side are visible, two segmentation lines are generated simultaneously. Figure 3 As shown.

[0109] Therefore, multiple dividing lines can be obtained, such as Figure 4 As shown, the dividing line is a dividing surface in three-dimensional space, and appears as a dividing line in a video frame.

[0110] S5. Recursively divide the space of each monitoring device by dividing lines, and add geographical constraints to estimate the spatial location of each monitoring device.

[0111] This step specifically includes:

[0112] S51. Determine the initial space of the monitoring equipment based on prior knowledge of the monitoring equipment.

[0113] Surveillance equipment is typically deployed along roadsides or at road intersections. Surveillance cameras in outdoor public places such as plazas and parks are usually deployed on the facades of nearby buildings or on tall lampposts. Based on this prior knowledge, and considering the limited field of view of surveillance equipment and the fact that surveillance equipment is usually located near landmarks, we first determine a general area with a high probability of containing surveillance equipment as the initial spatial location. Figure 2 As shown.

[0114] S52. For a dividing line, process it as follows: use the dividing line to perform binary space division on the initial space, and according to the direction of the dividing line, retain the half space where the monitoring equipment is located in the binary space division result as the initial space for the next dividing line division.

[0115] The steps for binary space partitioning are as follows:

[0116] Finding the intersection points of dividing lines and segmented regions: Calculating the intersection points of dividing lines and segmented regions is essentially finding the intersection points between a straight line and each edge (line segment) inside a polygon. Given an initial space, calculate the expressions for each edge of the polygon in that initial space, and then find the intersection points with the dividing lines.

[0117] Polygon Retention Selection After Segmentation: The visibility information of the markers is used to determine the polygon area to be retained after each segmentation. Based on the visible side of the marker from the camera's perspective, it is determined which side of the segmentation line the monitoring camera should be located on, and the half-space on this side is retained as the initial space for the next segmentation.

[0118] Polygon point set sorting: Calculate the coordinates of the polygon center based on the polygon point set. Draw a unit vector parallel to the X-axis through the centroid O. Calculate the angle between OP and Ox, where point P refers to the coordinates of each vertex of the polygon. Vertices with larger angles are located counterclockwise from those with smaller angles. The resulting half-space is used as the initial space for the next segmentation until no more segmentation lines are found, thus obtaining the region closest to the camera.

[0119] S53. After all the dividing lines have been used to divide the space, the outlines of buildings and objects in the space are added as geographical constraints to obtain an irregular polygon, such as... Figure 5 As shown.

[0120] When relevant data such as geospatial elements (e.g., building outlines) are available, using them as geographical constraints can further narrow down the space where the camera is located.

[0121] S54. Obtain the centroid of the irregular polygon as the spatial location of the monitoring device.

[0122] Since the spatial region formed by multiple divisions is usually an irregular polygon, in order to ensure that the estimated spatial location is inside the polygon, the centroid of the last polygon is selected as the spatial location of the monitoring device.

[0123] S6. Estimate the azimuth angle of each monitoring device based on the spatial location of each monitoring device and the start and end pose data of the marker appearing within the monitoring device's range.

[0124] In a camera imaging model, the camera's field of view is typically a cone, which projects onto a plane as a triangular region. The line containing the camera's optical axis is the angle bisector of this triangle. Therefore, based on the camera's spatial position calculation, the times when a marker enters and leaves the camera's field of view can be obtained by recognizing the monitored image. Using the planar triangle formed by the marker's spatial position at the moments of entry and exit and the estimated camera spatial position, the azimuth angle of the line containing the camera's optical axis can be calculated from the known coordinates of the marker when it enters and leaves the camera's field of view. Therefore, the steps for calculating the azimuth angle are: obtain the starting point position when the marker first appears within the monitoring device's range from the pose data, and the ending point position when it leaves the monitoring device's range; based on the monitoring device's spatial position and the obtained data, calculate the monitoring device's azimuth angle using the following formula:

[0125]

[0126] In the formula, (x s ,y s ) represents the spatial location of the monitoring equipment, set as point O, α OPLet α be the azimuth angle of the monitoring equipment, (x1, y1) represent the starting point (point A), (x2, y2) represent the ending point (point B), and α be the azimuth angle of the monitoring equipment. OA α represents the azimuth angle between the monitoring equipment and the starting point. OB Indicates the azimuth angle between the monitoring equipment and the endpoint, such as Figure 6 As shown.

[0127] Example 2

[0128] This embodiment provides a monitoring equipment positioning device, such as... Figure 7 As shown, it includes:

[0129] The first data acquisition module is used to acquire monitoring video data captured by each monitoring device when the marker moves along the space where each monitoring device is located;

[0130] The second data acquisition module is used to acquire the pose data of the marker as it moves along the space where each monitoring device is located;

[0131] The identification module is used to query the video frames in the monitoring video data in which the marker appears and the corresponding monitoring devices, and to identify the visible faces of the marker from the video frames;

[0132] The segmentation line construction module is used to construct segmentation lines by combining the visible face and pose data of the identified landmarks;

[0133] The location estimation module is used to recursively divide the space of each monitoring device by the dividing line, add geographical constraints, and estimate the spatial location of each monitoring device.

[0134] The azimuth estimation module is used to estimate the azimuth of each monitoring device based on the spatial location of each monitoring device and the start and end positions of the markers within the monitoring device's range.

[0135] This device corresponds one-to-one with the method in Embodiment 1. For details not covered herein, please refer to Embodiment 1.

[0136] It is worth noting that the modules included in the above system embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional module are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0137] Example 3

[0138] This embodiment also provides a monitoring equipment positioning system, such as Figure 8 As shown, it includes:

[0139] The marker, which contains a positioning sensor, such as a GNSS sensor, has a cuboid structure and each side is a different color;

[0140] The carrier is used to transport the markers along the space where each monitoring device is located; it can be a movable object such as a car or flatbed truck that carries the markers.

[0141] Surveillance equipment, used to capture video data of the movement of markers; and

[0142] The monitoring equipment positioning device in Example 2.

[0143] Finally, simulation verification was conducted on this invention, with the North Campus of Nanjing Normal University's Xianlin Campus selected as the experimental area. Figure 9 As shown, the area covers approximately 113,188 square meters. 2 The area contains basic geographical elements such as roads, shopping malls, playgrounds, school buildings, power distribution rooms, squares, and bus stops, which conform to the common characteristics of outdoor surveillance camera deployment scenarios. There are 47 video surveillance cameras in the test area, dispersed at road intersections and building facades. The system was implemented using C++ on a Windows 10 64-bit system. Without knowing the camera locations, both measurement methods and the method proposed in this paper were used to measure the locations of the surveillance cameras in the northern area. The results are as follows... Figure 10 As shown, this invention accurately locates the monitoring equipment.

Claims

1. A method for locating monitoring equipment, characterized in that, Includes the following steps: (1) Acquire the monitoring video data captured by each monitoring device when the marker moves along the space where each monitoring device is located; (2) Acquire the pose data of the marker as it moves along the space where each monitoring device is located; (3) Extract the video frames containing the markers and the corresponding monitoring devices from the monitoring video data, and identify the visible faces of the markers from the video frames; (4) Construct a dividing line by combining the visible surface and pose data of the identified landmarks; (5) Recursively divide the space of each monitoring device by dividing the dividing line, and add geographical constraints to estimate the spatial location of each monitoring device; (6) Estimate the azimuth angle of each monitoring device based on the spatial location of each monitoring device and the start and end pose data of the marker appearing within the monitoring device range; Step (2) specifically includes: (2-1) Obtain the positioning data of the marker as it moves along the space where each monitoring device is located; (2-2) Parse the positioning data to obtain the position of the marker at each collection moment during its movement along the space where each monitoring device is located; (2-3) Based on the positioning data and the position of the marker at each acquisition time, calculate the azimuth angle of the marker at each acquisition time, and use the position and azimuth angle of the marker as the pose data of the marker; Step (4) includes: (4-1) Obtain the marker pose information corresponding to the video frame in which the marker appears from the marker pose data. Specifically, the marker pose information corresponding to the video frame is the marker pose information whose time is consistent with the timestamp of the video frame. (4-2) Based on the visible faces of the landmarks in each video frame and the corresponding landmark pose information, construct the dividing line according to the following formula: ， Where x and y represent variables in the segmentation line equation, (x0, y0) represents the position coordinates in the marker pose information, and k is the azimuth parameter. If the visible side of the marker in the video frame is only the side, k is the tangent of the azimuth angle of the device's forward direction. If only the front is visible, then k is the tangent of the azimuth angle of the segmentation line generated on the front side of the device at a 90-degree angle to the forward direction. If both the front and side are visible, then two segmentation lines are generated simultaneously. Step (5) includes: (5-1) Determine the initial space of the monitoring equipment based on prior knowledge of the monitoring equipment; (5-2) For a dividing line, process it as follows: use the dividing line to perform binary space division on the initial space, and according to the direction of the dividing line, retain the half space where the monitoring equipment is located in the binary space division result as the initial space for the next dividing line. (5-3) For the space remaining after all the dividing lines have been completed, add the building outlines and object outlines in the space as geographical environment constraints to obtain an irregular polygon. (5-4) Obtain the centroid of the irregular polygon as the spatial location of the monitoring device; Step (6) includes: (6-1) Obtain the starting point of the marker when it first appears in the range of the monitoring equipment, and the ending point when it leaves the range of the monitoring equipment from the pose data; (6-2) Based on the spatial location of the monitoring equipment and the data obtained in step (6-1), calculate the azimuth angle of the monitoring equipment using the following formula: ， In the formula, (x s ,y s () represents the spatial location of the monitoring equipment, set as point O. Let (x1, y1) represent the azimuth angle of the monitoring equipment, (x1, y1) represent the starting point (point A), and (x2, y2) represent the ending point (point B). Indicates the azimuth angle between the monitoring equipment and the starting point. This indicates the azimuth angle between the monitoring equipment and the termination point.

2. The monitoring equipment positioning method according to claim 1, characterized in that: The surveillance video data includes a sequence of surveillance video frames and a timestamp for each surveillance video frame.

3. The method for locating monitoring equipment according to claim 1, characterized in that: Step (3) includes: (3-1) Obtain the monitoring device and timestamp corresponding to each video frame in the monitoring video data; (3-2) Based on the position data acquisition time of the marker, obtain video frames with the same time stamp; (3-3) Convert the acquired video frames to the HSV color gamut space; (3-4) For video frames in the HSV color gamut space, the contour recognition method is used to identify whether there are markers, and video frames with markers are retained; (3-5) Based on the color information of the markers in the retained video frames, identify the visible faces of the markers in the video frames, wherein each face of the marker has a different color.

4. The method for locating monitoring equipment according to claim 1, characterized in that: The method for calculating the azimuth angle of the marker at each acquisition time is as follows: ， In the formula, These represent the heading angle and pitch angle of the marker, respectively. These represent the coordinate difference between the primary and secondary positioning sensors in the northeast-central coordinate system at the time of marker acquisition.

5. A monitoring equipment positioning device for performing the method of claim 1, characterized in that, include: The first data acquisition module is used to acquire monitoring video data captured by each monitoring device when the marker moves along the space where each monitoring device is located; The second data acquisition module is used to acquire the pose data of the marker as it moves along the space where each monitoring device is located; The identification module is used to query the video frames in the monitoring video data in which the marker appears and the corresponding monitoring devices, and to identify the visible faces of the marker from the video frames; The segmentation line construction module is used to construct segmentation lines by combining the visible face and pose data of the identified landmarks; The location estimation module is used to recursively divide the space of each monitoring device by the dividing line, add geographical constraints, and estimate the spatial location of each monitoring device. The azimuth estimation module is used to estimate the azimuth of each monitoring device based on the spatial location of each monitoring device and the start and end positions of the markers within the monitoring device's range.

6. A positioning system for monitoring equipment, characterized in that, include: A marker, wherein a positioning sensor is installed inside the marker; A carrier for transporting the markers along the spatial arrangement of each monitoring device; Surveillance equipment is used to capture video data of the movement of markers; as well as The monitoring equipment positioning device as described in claim 5.

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