Communication tower-oriented remote video monitoring method, system and device

Abstract

The application relates to the technical field of target tracking, in particular to a remote video monitoring method, system and device for a communication tower. The method comprises the following steps: collecting a feature tracking image and a brightness image; determining a shadow change degree based on a connected domain of the brightness image and a frame difference of a feature difference frame image, so as to form a shadow change set; extracting a connected domain of the feature interpolation frame image itself; determining a moving change confusion degree based on the value and quantity of the frame difference in the connected domain itself; determining a tracking mark value based on the moving change confusion degree and the frame difference; marking a potential tracking area based on the comparison between the tracking mark value and the shadow change degree; determining a center position through the marking, taking the center position as a monitoring tracking position, and completing video monitoring. The application can accurately lock and track a real target, and provides reliable guarantee for the safety monitoring of the communication tower.

Description

Technical Field

[0001] This application relates to the field of target tracking technology, specifically to remote video monitoring methods, systems, and devices for communication towers. Background Technology

[0002] Communication towers are tall steel structures specifically built to support communication antennas and other equipment, ensuring stable transmission and wide coverage of communication signals. Installing security video surveillance systems on communication towers effectively prevents vandalism, theft, and other illegal activities, ensuring the safe and stable operation of the towers and their mounted equipment, and maintaining normal communication network services. Simultaneously, it can track potentially dangerous behaviors such as climbing the tower in real time, promptly detecting and stopping them, thereby minimizing security risks and comprehensively protecting the safety of communication towers, ensuring the stability and reliability of communication infrastructure.

[0003] Traditionally, tracking dangerous behaviors involves calculating differential frames using inter-frame difference algorithms and then performing morphological operations on these frames to determine the location and region of the dangerous behavior. However, because communication towers are installed outdoors, moving objects near the towers, such as vehicles, people, and animals, cast significant shadows. These shadows change position as the objects move, giving them dynamic characteristics. This can cause inter-frame difference algorithms to misidentify shadows as areas of dangerous behavior, reducing the accuracy of dangerous behavior tracking. Summary of the Invention

[0004] To address the technical problem of low tracking accuracy, this application provides a remote video monitoring method, system, and device for communication towers. The specific technical solution adopted is as follows:

[0005] This application proposes a remote video monitoring method for communication towers, which includes the following steps:

[0006] Video is acquired through a monitoring system, and each frame of video image is converted into a feature tracking image and a brightness image;

[0007] The brightness image and feature tracking image at adjacent time points are subtracted to obtain the brightness difference frame image and the feature difference frame image. The brightness difference frame image is binarized and the connected components are extracted and used as the first connected component of the feature difference frame image. The inter-frame range sequence is obtained by sorting the range values ​​of the inter-frame differences in the first connected component, and the inter-frame differences are extracted to form a shadow change set. The shadow change degree of the shadow part is determined according to the range and skewness of all element values ​​in the shadow change set.

[0008] The connected components of the extracted feature difference frame image are denoted as the potential tracking region; the motion change disorder of the potential tracking region is determined based on the number of all different inter-frame differences in the potential tracking region and the range of the inter-frame differences.

[0009] The tracking label value of a potential tracking region is determined by the range of all inter-frame differences in each potential tracking region and the motion variation disorder of the potential tracking region; the tracking label value is compared with the shadow variation to assign different labels to all potential tracking regions;

[0010] The tracking image is obtained by replacing all potential tracking areas with labels. After preprocessing, connected component operations are performed to obtain the target tracking area. The center position is determined based on the coordinates of the target tracking area and used as the monitoring and tracking position to complete video monitoring.

[0011] In the above scheme, this application calculates the inter-frame shadow variation degree of the shadow region and the motion variation disorder degree of the potential tracking region, and determines the tracking marker value accordingly. This effectively distinguishes between shadow changes and image changes caused by the movement of moving objects, avoiding the problem of reduced tracking accuracy due to shadow misjudgment in traditional inter-frame difference algorithms. It significantly improves the accuracy of tracking dangerous objects, ensuring accurate locking and tracking of the real target, and providing reliable protection for the safety monitoring of communication towers. By analyzing the distribution of potential tracking regions in the feature difference frame image and comprehensively considering factors such as the range of inter-frame differences and the number of different values ​​to calculate the motion variation disorder degree, this invention can more sensitively identify inter-frame differences caused by the movement of moving objects. Even in complex environments and lighting conditions surrounding communication towers, the system can accurately distinguish targets that need to be tracked, despite the chaotic distribution of targets. This enhances the system's ability to identify dangerous targets and helps to promptly detect potential security threats. After determining the target tracking area, the system draws a target detection area bounding box and calculates its center position. By adjusting the camera's pan-tilt unit using the camera's control interface, the camera's center is aligned with the center of the target detection area bounding box. This allows for quick and accurate aiming of the camera at the target tracking area, achieving efficient positioning and tracking of dangerous objects. This significantly improves the response speed and tracking efficiency of the remote video monitoring system for communication towers, enabling the tracking and handling of dangerous behaviors in the first instance and effectively reducing security risks.

[0012] In one embodiment, the inter-frame difference is the grayscale difference at the same location in two adjacent frames.

[0013] In one embodiment, the method for obtaining the inter-frame range sequence based on the range values ​​of inter-frame differences in the first connected component, and extracting the inter-frame differences to form a shadow change set, is as follows:

[0014] Arrange the range values ​​of the inter-frame differences of all first connected components in ascending order to obtain the inter-frame range sequence, and form a shadow change set by taking the inter-frame differences corresponding to the first preset number of range values.

[0015] In one embodiment, the degree of shading variation is positively correlated with the range of element values ​​and the skewness of element values, respectively.

[0016] In one embodiment, the degree of motion variation disorder is positively correlated with the number of inter-frame differences and the range of inter-frame differences.

[0017] In one embodiment, the tracking marker value is positively correlated with the range of all inter-frame differences in the potential tracking region and the degree of motion variation disorder in the potential tracking region, respectively.

[0018] In one embodiment, the method of comparing tracking marker values ​​and shadow variability to assign different markers to all potential tracking areas is as follows:

[0019] If the tracking marker value is greater than the shadow variation of the shadowed area, the potential tracking area is marked as 1; otherwise, the potential tracking area is marked as 0.

[0020] In one embodiment, the method for determining the center position based on the coordinates of the target tracking area is as follows:

[0021] Obtain the maximum and minimum values ​​of the x-coordinate and y-coordinate in the connected component of the target. Construct a rectangular box using the maximum and minimum values ​​of the x-coordinate and y-coordinate as the target detection region box. Extract the coordinates of the center point of the target detection region box as the center position.

[0022] Secondly, this application proposes a remote video monitoring system for communication towers, which includes a video acquisition module, a data transmission module, a data analysis module, and a target tracking module, to implement the steps of any one of the remote video monitoring methods for communication towers described above.

[0023] Thirdly, embodiments of this application also provide a remote video monitoring device for communication towers, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described remote video monitoring methods for communication towers.

[0024] The beneficial effects of this application are as follows:

[0025] This application calculates the inter-frame shadow variation degree of the shadow region and the motion variation disorder degree of the potential tracking region, and determines the tracking marker value accordingly. This effectively distinguishes between shadow changes and image changes caused by the movement of moving objects, avoiding the problem of reduced tracking accuracy due to shadow misjudgment in traditional inter-frame difference algorithms. It significantly improves the accuracy of tracking dangerous objects, ensuring precise locking and tracking of the true target, and providing reliable protection for the safety monitoring of communication towers. By analyzing the distribution of potential tracking regions in the feature difference frame image and comprehensively considering factors such as the range of inter-frame differences and the number of different values ​​to calculate the motion variation disorder degree, this invention can more sensitively identify the distribution disorder of inter-frame differences caused by the movement of moving objects. Even in complex environments and lighting conditions surrounding communication towers, the system can accurately distinguish targets to be tracked, enhancing its ability to identify dangerous targets and helping to promptly detect potential security threats. After determining the target tracking area, the system draws a target detection area bounding box and calculates its center position. By adjusting the camera's pan-tilt unit using the camera's control interface, the camera's center is aligned with the center of the target detection area bounding box, quickly and accurately aligning the camera with the target tracking area. This achieves efficient positioning and tracking of dangerous objects, significantly improving the response speed and tracking efficiency of the remote video monitoring system for communication towers. It enables the tracking and handling of dangerous behaviors in the first instance, effectively reducing security risks. Attached Figure Description

[0026] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a remote video monitoring method for communication towers, provided in one embodiment of this application;

[0028] Figure 2 This is a flowchart of a remote video monitoring system for communication towers provided as an embodiment of this application. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, structures, features, and effects of the remote video monitoring method, system, and apparatus for communication towers proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0031] Examples of remote video monitoring methods, systems, and devices for communication towers:

[0032] The following description, in conjunction with the accompanying drawings, details the specific solutions for the remote video monitoring method, system, and device for communication towers provided in this application.

[0033] Please see Figure 1 The diagram illustrates a flowchart of a remote video monitoring method for communication towers according to an embodiment of this application. The method includes the following steps:

[0034] Step S001: Acquire feature tracking image and brightness image.

[0035] A remote video surveillance device is installed in the communication tower. This device includes surveillance cameras positioned at multiple angles; in this example, each camera has a 60-degree monitoring angle, meaning six cameras are evenly installed around the perimeter of the tower. The data collected by the cameras is then transmitted through the tower to a remote video surveillance system. The video is then acquired via this system.

[0036] Each frame of the video image is converted into a grayscale image using a weighted average method. Since noise may exist in the images during acquisition, and the communication towers in the monitored area have local similarity to the background, image denoising is necessary. In this embodiment, the Non-local Means (NLM) algorithm is used to denoise the grayscale image, and the denoised image is designated as the feature tracking image. The calculation of the weighted average method and the NLM algorithm are well-known techniques, and the specific calculation steps will not be elaborated here.

[0037] Furthermore, the video image is converted to the HSV color space, and the "Value" component is extracted to represent the luminance of the video image. The calculation of converting RGB images to HSV images is a well-known technique, and the specific calculation steps will not be described in detail here.

[0038] At this point, the feature tracking image and brightness image of each video image have been obtained.

[0039] Step S002: Based on the connected components of the brightness image, the inter-frame differences of the feature difference frame image constitute a set of shadow changes, thereby determining the degree of shadow change.

[0040] Regarding the environment surrounding communication towers, when people or animals exhibit dangerous behavior towards the tower, their movement causes color changes in adjacent frames captured by surveillance cameras, affecting the brightness of those areas and creating uneven variations in brightness. While the shadows of moving objects also affect color changes across different frames, their impact on brightness remains consistent, without altering the area's shape or shape.

[0041] The brightness difference frame image is obtained by subtracting the brightness image from the previous time step, and the feature tracking image from the previous time step is subtracted from the current time step. This is then denoted as the feature difference frame image. The brightness difference frame image is then used as input to a binarization algorithm, and the algorithm's output is denoted as the inter-frame binarized image. The calculation of the image difference and the image binarization algorithm are well-known techniques, and the specific calculation process will not be elaborated here.

[0042] Because the time difference between adjacent frames is small, the brightness change of shadows between the two frames is very small. Simultaneously, the movement of the shadow area is minimal, resulting in shadows only existing at the boundaries in the inter-frame binarized image. Furthermore, each object has a shadow area, but the brightness change across different regions is the same, and the inter-frame difference is small compared to moving objects. Therefore, a connected component labeling algorithm is used to identify all connected components in the inter-frame binarized image. In this example, the Two-Pass algorithm is used. Note: When no connected components exist, it indicates that different moving objects do not require processing.

[0043] For the feature difference frame image, the connected components obtained above are represented in the feature difference frame image as the first connected components. The maximum and minimum inter-frame differences of each first connected component are obtained, and the range of the inter-frame differences of the first connected components is calculated. Since the inter-frame difference changes of the moving shadow part are the same or close, and smaller than the inter-frame difference changes of the moving object, the range values ​​of the inter-frame differences of all first connected components are arranged from smallest to largest to obtain the inter-frame range sequence. Because there is a moving object, it means that there is a moving shadow. There is a one-to-one correspondence between the moving object and the moving shadow. The inter-frame differences corresponding to the first n% of the range values ​​in the inter-frame range sequence constitute the shadow change set; the element value of the element in the shadow change set is the inter-frame difference. This is used to characterize the inter-frame difference change that the shadow affects the object. The inter-frame difference is the grayscale difference at the same position in two adjacent frames. In this embodiment, n is 50.

[0044] The degree of shadow variation in the shadowed area is determined by the range and skewness of all element values ​​in the shadow variation set.

[0045] The degree of shadow variation is positively correlated with the range of element values ​​and the skewness of element values, respectively.

[0046] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by the actual application, and this application does not impose any special restrictions.

[0047] Preferably, in this embodiment, the expression for the degree of shadow variation is:

[0048] , This represents the range of all element values ​​in the shaded variation set. This represents the skewness of all element values ​​in the shaded variation set. It is the arctangent function. As a regulating factor, This represents the degree of shadow variation. Where, in order to... The range of is (-1, 1), in this embodiment The value is .

[0049] In calculating the shadow changes in the shaded area, the change states of different shaded areas are the same. Therefore, the difference between the change from shaded to non-shaded areas and the change from non-shaded areas is the same, which means that the range of all elements in the shadow change set is small. At the same time, the greater the skewness of all elements in the shadow change set, the more the data is biased to the right of the mean. Therefore, it is necessary to increase the range of all elements in the shadow change set to reduce the impact of individual noise on the data.

[0050] At this point, the degree of shadow variation in the shadowed area has been obtained.

[0051] Step S003: Extract the connected components of the feature difference frame image itself; determine the degree of motion change disorder based on the value and quantity of inter-frame differences in its own connected components.

[0052] The feature difference frame image is used as input to the connected component labeling algorithm, and the output is all connected components in the feature difference frame image, denoted as the second connected component. Each second connected component is designated as a potential tracking region. For each potential tracking region, when the region is caused by a moving object, drastic changes occur within the potential tracking region, and the numerical distribution of inter-frame differences is relatively chaotic. However, when the region is composed of shaded areas, the distribution of inter-frame differences in the potential tracking region is relatively uniform, and the differences between inter-frame differences are small, so this region does not need to be tracked.

[0053] For each potential tracking region, the motion variation disorder of the potential tracking region is determined based on the number of all different inter-frame differences within it and the range of the inter-frame differences.

[0054] The degree of motion variation disorder is positively correlated with the number of inter-frame differences and the range of inter-frame differences.

[0055] Preferably, in this embodiment, the expression for the degree of movement change disorder is:

[0056] , This represents the range of inter-frame differences in the i-th potential tracking region. This represents the number of inter-frame differences in the i-th potential tracking region. This represents the degree of movement and change disorder in the i-th potential tracking region.

[0057] The larger the range of inter-frame differences within a potential tracking region and the greater the number of different values, the more chaotic the distribution of inter-frame differences within that region. This indicates that the potential tracking region is more likely to be affected by inter-frame difference changes caused by moving objects. However, since inter-frame difference changes caused by shadows are a single type of change, the range of inter-frame differences and the number of different values ​​within the tracking region are both relatively small. This results in less chaotic movement changes within the potential tracking region, indicating that the potential tracking region does not need to be tracked. The video camera should track areas with greater chaotic movement changes to ensure tracking accuracy.

[0058] At this point, the degree of motion and change disorder in the potential tracking area has been obtained.

[0059] Step S004: Determine the tracking marker value based on the motion change disorder and inter-frame difference; and mark the potential tracking area based on the tracking marker value and the shadow change degree comparison.

[0060] Both shadowed areas and moving object areas affect the changes in adjacent frames of the video. However, the inter-frame difference changes caused by shadowed areas are similar or nearly identical, with smaller differences; that is, the inter-frame shadow variation in shadowed areas is smaller. In contrast, the inter-frame difference changes caused by moving object areas are more chaotic, resulting in a smaller value for the chaos of motion changes in the potential tracking area. Furthermore, the inter-frame differences caused by moving object areas are more chaotic and have larger numerical differences than those caused by shadowed areas.

[0061] Therefore, when the motion variation disorder of the potential tracking region is greater than that of the shadow variation set, it indicates that the potential tracking region is a region containing a moving object. This is because the motion of the moving object causes large fluctuations and irregular changes in the inter-frame difference distribution within the region, while shadow variations are relatively stable. By analyzing the motion variation disorder of the potential tracking region, moving targets can be identified more accurately, improving the accuracy and reliability of tracking.

[0062] Based on this, the tracking marker value of a potential tracking region is determined by the range of all inter-frame differences and the motion change disorder in each potential tracking region.

[0063] The tracking marker values ​​are positively correlated with the range of all inter-frame differences and the degree of motion variation disorder in the potential tracking region, respectively.

[0064] Preferably, the expression for the tracking marker value is:

[0065] , This represents the range of inter-frame differences in the i-th potential tracking region. This represents the degree of disorder in the movement and change of the i-th potential tracking region. This represents the tracking marker value for the i-th potential tracking region.

[0066] The tracking marker value of each potential tracking region is compared with the shadow variation of the shadowed area; thus, the potential tracking regions are marked according to whether there is motion of a moving object.

[0067] Preferably, in this embodiment, if the tracking mark value is greater than the shadow change degree of the shadow area, that is, there is movement of a moving object in the potential tracking area, the potential tracking area is marked as 1; otherwise, the potential tracking area is marked as 0.

[0068] When the degree of motion variation in the potential tracking region is greater than the degree of shadow variation in the shadow area, it indicates that the degree of disorder in the inter-frame difference distribution in the potential tracking region is significantly higher than the fluctuation of inter-frame difference caused by shadow changes. This usually indicates the presence of human or animal movement in the region. This is because the movement of humans or animals can cause drastic changes and irregular distributions of inter-frame differences, while shadow changes are relatively stable and the distribution of inter-frame differences is more uniform.

[0069] This completes the marking of potential tracking areas.

[0070] Step S005: Determine the center position by marking it and use it as the monitoring and tracking position to complete video monitoring.

[0071] The tracking marker values ​​of the potential tracking area calculated above are used to obtain the area tracking image.

[0072] In this embodiment, the pixel values ​​of all pixels in the feature difference frame image corresponding to all potential tracking regions with a tracking marker value of 1 are set to 1, and the pixel values ​​of pixels in other regions are marked as 0, which is denoted as the region tracking image.

[0073] Because differential frame images may cause discontinuities in the edge regions of moving people or animals, the region tracking image undergoes dilation followed by erosion. Then, connected components of the same object are connected, and the resulting image is denoted as the target tracking image. Furthermore, the target tracking image is used as input to a connected component labeling algorithm, and the output is the connected components of each target in the target tracking image. These connected components represent the target tracking areas that require remote video monitoring in tower communication.

[0074] Finally, the remote video monitoring system on the communication tower draws target detection region boxes at the locations of the target connected domains. The method for drawing the target detection region boxes is as follows: The maximum and minimum values ​​of the x-coordinate and y-coordinate in each target connected domain are obtained, and four points are formed: (maximum x-coordinate, maximum y-coordinate), (maximum x-coordinate, minimum y-coordinate), (minimum x-coordinate, maximum y-coordinate), and (minimum x-coordinate, minimum y-coordinate). These four points are then connected to form the target detection region box. The center position of the target detection region box is calculated and used as input to the target tracking module of the video surveillance camera. The target tracking module calculates the offset between the center position of the target detection region box and the center position of the surveillance camera, and adjusts the camera's pan / tilt unit through the camera's control interface (such as the PTZ control protocol) to align the camera's center with the center position of the target detection region box. This enables the tracking of dangerous objects. If multiple target connected domains exist, the average value of the center positions of all target detection region boxes is used as the movement center. The calculation of the offset is a well-known technique, and the specific calculation steps will not be elaborated here.

[0075] This enabled remote video monitoring of communication towers.

[0076] See Figure 2 The diagram illustrates a flowchart of a remote video monitoring system for communication towers according to an embodiment of this application.

[0077] A remote video surveillance system for communication towers includes a video acquisition module, a data transmission module, a data analysis module, and a target tracking module. The video acquisition module collects video data from the vicinity of the communication tower; the data transmission module transmits the collected video; the data analysis module calculates the center position of the monitored target; and the target tracking module rotates the surveillance camera horizontally and vertically to align its center with the center of the monitored target.

[0078] Based on the same inventive concept as the above methods, embodiments of the present invention also provide a remote video monitoring device for communication towers, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the methods described above for remote video monitoring of communication towers.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0080] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A remote video monitoring method for communication towers, characterized in that, The method includes the following steps: Video is acquired through a monitoring system, and each frame of video image is converted into a feature tracking image and a brightness image; The brightness image and feature tracking image at adjacent time points are subtracted to obtain the brightness difference frame image and the feature difference frame image. The brightness difference frame image is binarized and the connected components are extracted and used as the first connected component of the feature difference frame image. The inter-frame range sequence is obtained by sorting the range values ​​of the inter-frame differences in the first connected component, and the inter-frame differences are extracted to form a shadow change set. The shadow change degree of the shadow part is determined according to the range and skewness of all element values ​​in the shadow change set. The connected components of the extracted feature difference frame image are denoted as the potential tracking region; the motion change disorder of the potential tracking region is determined based on the number of all different inter-frame differences in the potential tracking region and the range of the inter-frame differences. The tracking label value of a potential tracking region is determined by the range of all inter-frame differences in each potential tracking region and the motion variation disorder of the potential tracking region; the tracking label value is compared with the shadow variation to assign different labels to all potential tracking regions; By labeling the pixel values ​​of all pixels corresponding to the feature difference frame image of all potential tracking regions, the region tracking image is obtained. After preprocessing, connected component operation is performed to obtain the target tracking region. The center position is determined based on the coordinates of the target tracking region and used as the monitoring and tracking position to complete video monitoring. The method for obtaining the inter-frame range sequence based on the range values ​​of inter-frame differences in the first connected component, and extracting the inter-frame differences to form a shadow change set, is as follows: Arrange the range values ​​of the inter-frame differences of all first connected components in ascending order to obtain the inter-frame range sequence, and form a shadow change set by taking the inter-frame differences corresponding to the first preset number of range values.

2. The remote video monitoring method for communication towers as described in claim 1, characterized in that, The inter-frame difference refers to the grayscale difference at the same location in two adjacent frames.

3. The remote video monitoring method for communication towers as described in claim 1, characterized in that, The degree of shadow variation is positively correlated with the range of element values ​​and the skewness of element values, respectively.

4. The remote video monitoring method for communication towers as described in claim 1, characterized in that, The degree of motion variation disorder is positively correlated with the number of inter-frame differences and the range of inter-frame differences.

5. The remote video monitoring method for communication towers as described in claim 1, characterized in that, The tracking marker values ​​are positively correlated with the range of all inter-frame differences in the potential tracking region and the degree of motion variation disorder in the potential tracking region.

6. The remote video monitoring method for communication towers as described in claim 1, characterized in that, The method for comparing tracking marker values ​​and shadow variation to assign different markers to all potential tracking areas is as follows: If the tracking marker value is greater than the shadow variation of the shadowed area, the potential tracking area is marked as 1; otherwise, the potential tracking area is marked as 0.

7. The remote video monitoring method for communication towers as described in claim 1, characterized in that, The method for determining the center position based on the coordinates of the target tracking area is as follows: Obtain the maximum and minimum values ​​of the x-coordinate and y-coordinate in the connected component of the target. Construct a rectangular box using the maximum and minimum values ​​of the x-coordinate and y-coordinate as the target detection region box. Extract the coordinates of the center point of the target detection region box as the center position.

8. A remote video monitoring system for communication towers, characterized in that, It includes a video acquisition module, a data transmission module, a data analysis module, and a target tracking module, used to implement the steps of the remote video monitoring method for communication towers as described in any one of claims 1-7.

9. A remote video monitoring device for communication towers, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the remote video monitoring method for communication towers as described in any one of claims 1-7.

Images