Snapshot method and device of separated visual equipment, computer equipment and medium

Abstract

The invention relates to the technical field of video monitoring, and discloses a snapshot method and device of a separated visual device, a computer device and a medium, and the method comprises the steps: when the visual device is in a dormant state, detecting a moving object in a first preset range through a separated radar device, the separated radar device is connected with the visual equipment through a wireless link; when the separated radar device detects the moving object, a wake-up instruction is sent to the visual equipment, so that the visual equipment triggers a preset snapshot mechanism according to the wake-up instruction; monitoring an environment view in a second preset range through a preset snapshot mechanism of the visual equipment; and executing a snapshot operation on the moving object meeting the preset condition in the environment view to obtain a target snapshot image. According to the invention, the problems of long-distance triggering failure and complex scene monitoring blind areas caused by mismatching of the detection range of the sensor and the field angle of the camera in the existing intelligent cat eye system are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of video surveillance, and particularly to a capturing method, device, computer device and medium for a separable visualization device. Background Art

[0002] With the popularization of intelligent security technology, intelligent cat-eye systems have gradually become an important component of home security. Existing solutions usually adopt a low-power camera combined with a radar or a passive infrared sensor to work together: the cat-eye main body enters a low-power sleep state when there is no one, and wakes up the device to perform capturing when the radar or infrared sensor detects the movement of a nearby person. Although such a design can achieve basic monitoring in an open scenario, its triggering mechanism relying on a single sensor has significant limitations.

[0003] However, due to the mismatch between the detection range of the sensor and the field of view angle of the cat-eye in the prior art, there are blind spots in actual applications. Due to power consumption limitations, the radar or infrared sensor can only sense fast movements at short distances, while the field of view angle of the cat-eye camera can cover a range of more than 10 meters. If a person approaches slowly outside 5-10 meters or lingers in complex scenarios such as corridors and corners, the sensor cannot trigger capturing, resulting in the omission of key monitoring information. The integrated design further limits the flexibility of sensor layout, and it is difficult to solve the problem of long-distance triggering failure by expanding the detection range. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a capturing method, device, computer device and medium for a separable visualization device to solve the problems of long-distance triggering failure and monitoring blind spots in complex scenarios caused by the mismatch between the detection range of the sensor and the field of view angle of the camera in the existing intelligent cat-eye system.

[0005] In a first aspect, an embodiment of the present invention provides a capturing method for a separable visualization device, the method comprising:

[0006] When the visualization device is in a sleep state, detecting an active object within a first preset range through a separable radar device, wherein the separable radar device is connected to the visualization device through a wireless link;

[0007] When the separable radar device detects an active object, sending a wake-up instruction to the visualization device so that the visualization device triggers a preset capturing mechanism according to the wake-up instruction;

[0008] Monitoring the environmental view within a second preset range through the preset capturing mechanism of the visualization device;

[0009] Performing a capturing operation on an active object that meets preset conditions in the environmental view to obtain a target captured image.

[0010] Further, detecting an active object within a first preset range by means of the split radar device includes:

[0011] Using the split radar device to detect a target object within a first preset range, and calculating the real-time distance and movement direction between the target object and the split radar device;

[0012] According to the real-time distance and the movement direction, determining whether the movement trajectory of the target object meets a preset movement trajectory condition;

[0013] If the movement trajectory of the target object meets the preset movement trajectory condition, it is determined that there is an active object within the first preset range.

[0014] Further, sending a wake-up instruction to the visualization device includes:

[0015] Obtaining the installation position of the split radar device;

[0016] Determining the movement coordinates of the active object according to the installation position and the real-time distance;

[0017] Constructing a corresponding wake-up instruction by using the movement coordinates, and sending the wake-up instruction to the visualization device.

[0018] Further, monitoring the environmental vision within a second preset range through a preset capture mechanism of the visualization device includes:

[0019] Analyzing the movement coordinates in the wake-up instruction;

[0020] Adjusting the camera focal length of the visualization device according to the movement coordinates, and turning the visualization device to the aiming area corresponding to the movement coordinates;

[0021] Extracting the target contour of the active object in the environmental vision, and if the target contour conforms to the human contour, it is determined that the active object meets the preset conditions.

[0022] Further, the method further includes:

[0023] Deploying at least two split radar devices at different spatial positions, wherein each of the split radar devices independently detects active objects within a preset range;

[0024] When multiple split radar devices detect the movement trajectories of the same active object, fusing the multiple movement trajectories to obtain a three-dimensional movement path;

[0025] Predicting the timing characteristics of the active object approaching the visualization device in space according to the three-dimensional movement path, and adjusting the aiming area of the visualization device according to the timing characteristics.

[0026] Further, the method further includes:

[0027] When multiple separate radar devices detect different moving objects, controlling the separate radar devices to send multi-target position information to the visualization device;

[0028] Generating a dynamic warning area according to the multi-target position information, and calculating the distance weight between each moving object and the visualization device;

[0029] Assigning corresponding priorities to the moving objects based on the distance weight, configuring corresponding sampling rates according to the priorities, and controlling the visualization device to perform a capture operation according to the sampling rates.

[0030] Further, the method further includes:

[0031] Receiving the installation position information of each separate radar device and a first preset range input by the user;

[0032] Binding the installation position information and the first preset range to the visualization device for identification to generate a topological mapping relationship;

[0033] Dynamically adjusting the scanning frequencies of the separate radar devices according to the topological mapping relationship.

[0034] In a second aspect, an embodiment of the present invention provides a capture device for a separate visualization device, the device including:

[0035] A detection module, configured to detect moving objects within a first preset range through a separate radar device when the visualization device is in a sleep state, where the separate radar device is connected to the visualization device through a wireless link;

[0036] A sending module, configured to send a wake-up instruction to the visualization device when the separate radar device detects a moving object, so that the visualization device triggers a preset capture mechanism according to the wake-up instruction;

[0037] A monitoring module, configured to monitor the environmental field of view within a second preset range through the preset capture mechanism of the visualization device;

[0038] An execution module, configured to perform a capture operation on the moving objects that meet the preset conditions in the environmental field of view to obtain target capture images.

[0039] In a third aspect, an embodiment of the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method according to the first aspect or any corresponding embodiment thereof.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the method according to the first aspect or any corresponding embodiment thereof.

[0041] The method provided by the embodiments of the present application has the following beneficial effects:

[0042] The radar device independently deployed by the method provided by the embodiments of the present application expands the monitoring range (the first preset range), breaks through the distance limitation of traditional integrated sensors, can sense the movement of personnel in advance, and solves the problem of long-distance trigger failure; uses low-power wireless links such as Bluetooth to send wake-up instructions only when activities are detected, avoids the continuous operation of the cat's eye device, and significantly reduces the overall energy consumption of the system; after waking up, the cat's eye camera covers a larger field of view (the second preset range), and combines a preset capture mechanism to perform dynamic monitoring on complex scenarios (such as long corridors and corners), eliminating the blind spots caused by the mismatch between the fields of view of sensors and cameras in traditional solutions; filters effective targets through preset conditions (such as human shape recognition and motion trajectory analysis), reduces false touch captures, improves the effectiveness and storage efficiency of images, and at the same time supports multi-radar collaboration to expand the scene modeling ability and enhance the adaptability to complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 is a flowchart of a capture method for a split-type visualization device according to an embodiment of the present invention;

[0045] Figure 2 is a detection flowchart of a split-type radar device and a visualization device according to an embodiment of the present invention;

[0046] Figure 3 is a schematic diagram of the module connection relationship between a split-type visualization device and a radar device according to an embodiment of the present invention;

[0047] Figure 4It is a schematic diagram of the working process of a split-type visualization monitoring system according to an embodiment of the present invention;

[0048] Figure 5 It is a structural block diagram of the capture device of a split-type visualization device according to an embodiment of the present invention;

[0049] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] According to an embodiment of the present invention, there are provided a capture method, device, computer device, and medium for a split-type visualization device. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0052] In this embodiment, a capture method for a split-type visualization device is provided. Figure 1 It is a flowchart of the capture method for a split-type visualization device according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps:

[0053] Step S11, when the visualization device is in a sleep state, detect moving objects within a first preset range through a split-type radar device, where the split-type radar device is connected to the visualization device through a wireless link.

[0054] In the embodiment of the present application, when the system is in a normal state, the visualization device (i.e., the cat's eye main device) is in a sleep state. This is to minimize the system power consumption and extend the device battery life. In the sleep state, most functional modules of the device, such as the camera module, image processing module, WIFI module, etc., are in a power-off or low-power standby mode. Only the Bluetooth module remains working and runs in a low-power broadcast mode, waiting for the wake-up instruction from the split-type radar device. This sleep strategy ensures that the device does not consume too much power when there are no moving objects approaching, while being able to quickly respond to external trigger signals, achieving a balance between low power consumption and efficient monitoring.

[0055] It should be noted that, asFigure 2 As shown, when the visualization device is in a sleep state to reduce power consumption, the separate radar device (i.e., the sensor device in the figure) is connected to the visualization device via a wireless link (such as Bluetooth), detects moving objects within a first preset range (i.e., the area where people approach), and if the preset conditions are met, will send a wake-up instruction and detection data to the visualization device. During the device initialization phase, the user sets through the APP to pair the separate radar device with the visualization device and establish a Bluetooth connection relationship. After that, after the radar device detects a moving object and determines that it meets the preset conditions, it can send a wake-up instruction and relevant detection data to the visualization device through this Bluetooth link. The low-power characteristic of the Bluetooth connection ensures that excessive power consumption will not occur during long-term standby, and at the same time its stable communication performance can ensure the reliable transmission of instructions and data, providing guarantee for the normal operation of the system.

[0056] As Figure 3 shown, the module connection relationship between the separate visualization device and the radar device (sensor device and cat's eye main device). The sensor device includes a radar module and a Bluetooth module. After the radar module detects information, it transmits the data to the cat's eye main device through the Bluetooth module. The cat's eye main device sequentially receives the data through the Bluetooth module, then processes it through the cat's eye module, and finally performs subsequent operations such as network transmission of relevant data by the WIFI module, reflecting the signal transmission and processing process based on different modules between the two devices.

[0057] In the embodiment of the present application, detecting moving objects within a first preset range by the separate radar device includes the following steps A1 - A3:

[0058] Step A1, using the separate radar device to detect the target object within the first preset range, and calculating the real-time distance and movement direction between the target object and the separate radar device.

[0059] Specifically, the separate radar device uses technologies such as infrared or microwave radar to scan and monitor the first preset range. The radar emits signals and receives reflected signals, and determines whether there is a target object by analyzing the reflected signals. For example, microwave radar uses the Doppler effect. When an object moves within the monitoring range, the frequency of the reflected wave will change, and the radar identifies the target object by detecting this frequency change. By measuring the time interval from the emission to the reception of the radar signal and combining the propagation speed of the signal in the air, the real-time distance between the target object and the separate radar device can be calculated. For example, it is known that the propagation speed of microwave signals in the air is approximately the speed of light (3 * 10 8 m / s). If the radar emits a signal and receives the reflected signal after t seconds, the distance between the target object and the radar Determine the moving direction of the target object based on the position changes of the target object detected at consecutive moments. For example, if at two adjacent moments, the abscissa of the target object in the radar coordinate system increases and the ordinate remains unchanged, it can be preliminarily determined that the target object is moving in the positive x-axis direction.

[0060] Step A2, based on the real-time distance and the moving direction, determine whether the moving trajectory of the target object meets the preset moving trajectory condition.

[0061] Specifically, the preset moving trajectory condition is a rule set according to the actual application requirements and scenarios. For example, it is set that the target object approaches the position of the visualization device and the moving speed is within a certain range, or the moving trajectory conforms to a certain specific path pattern (such as approaching in a straight line, approaching in an arc, etc.), then it is considered to meet the preset condition.

[0062] For example, compare the distance and moving direction information of the target object calculated in real time with the preset conditions. If the moving direction of the target object is towards the visualization device, the distance is gradually decreasing, and its moving speed is also within the preset reasonable range, then it can be considered that the moving trajectory of the target object meets the preset moving trajectory condition. On the contrary, if the moving direction of the target object is away from the visualization device, or the distance does not change significantly, or the moving speed is too fast or too slow and does not meet the preset speed range, then it does not meet the preset moving trajectory condition.

[0063] Step A3, if the moving trajectory of the target object meets the preset moving trajectory condition, determine that there is an active object within the first preset range.

[0064] Specifically, when it is determined that the moving trajectory of the target object meets the preset condition, it can be considered that there is an active object within the first preset range. This means that the separated radar device has successfully detected the target object that meets the requirements, and subsequent operations need to be further triggered, such as sending a wake-up instruction to the visualization device so that the visualization device can perform actions such as taking a snapshot.

[0065] In an actual low-power cat-eye system, if someone slowly walks towards the door with a cat-eye installed from a distance, the separated radar device continuously detects its distance and moving direction, determines that the moving trajectory of this person meets the preset condition, that is, approaching the door direction and with normal speed, then determines that there is an active object, and then wakes up the cat-eye device for snapshot preparation, thus effectively solving the detection blind area problem existing in the traditional cat-eye system.

[0066] Step S12, when the separated radar device detects an active object, send a wake-up instruction to the visualization device so that the visualization device triggers a preset snapshot mechanism according to the wake-up instruction.

[0067] As an example, such as Figure 4As shown in the figure, the working process of the monitoring system composed of radar A, radar B, the main control module, and the cat's eye device. Radar A and radar B respectively detect the coordinates (x1, y1) and (x2, y2) of the target point and transmit them to the main control module, and the main control module uses Kalman filtering for trajectory fitting. When the target enters the warning area, the main control module sends a pre-wake-up instruction (Level2) to the cat's eye device, and the cat's eye device feeds back that it is ready; when the target further enters the core area, the main control module sends a full-function wake-up (Level3) instruction to the cat's eye device to achieve monitoring responses at different stages.

[0068] In the embodiment of the present application, sending a wake-up instruction to the visualization device includes the following steps B1 - B3:

[0069] Step B1, obtain the installation position of the split radar device.

[0070] Specifically, the installation position of the split radar device needs to be set according to the specific environment and monitoring requirements. For example, in a home scenario, in order to comprehensively monitor the area in front of the door, the radar device can be installed at the corner above the door; if it is applied to a hotel corridor, it can be installed on the walls at both ends of the corridor. The installation position can be obtained in the following ways: Manual input, after the installation is completed, the user can accurately input the installation position information of the radar device, such as the specific room number, floor information, and the orientation relative to the door or other fixed reference points, through the supporting APP on the map interface or input box. Automatic positioning, some advanced split radar devices are equipped with GPS (Global Positioning System) or other indoor positioning modules, which can automatically obtain their geographical location information and transmit this position information to the associated system device through Bluetooth or other wireless communication methods. For example, when the radar device is installed outdoors and has GPS function, it can determine its longitude and latitude coordinates in real time and upload them to the system. Topological mapping relationship acquisition, in the case where the topological mapping relationship has been pre-constructed, that is, the installation position information of each split radar device and the association relationship with the visualization device have been marked and bound. At this time, according to the unique identifier of the radar device, the corresponding installation position information can be quickly queried and obtained directly from the pre-stored topological mapping database.

[0071] Step B2, determine the moving coordinates of the moving object according to the installation position and the real-time distance.

[0072] Specifically, after obtaining the installation position of the split radar device, combined with the calculated real-time distance between the target object and the split radar device, the moving coordinates of the moving object can be determined. Here, a two-dimensional plane is used as an example for illustration. Assume that the coordinates of the installation position of the radar device are (x0, y0), the real-time detected distance between the target object and the radar device is d, and the angle between the moving direction of the target object and the positive x-axis direction is θ.

[0073] According to the trigonometric function relationship, the coordinate offset Δx = d×cosθ and Δy = d×sinθ of the target object relative to the radar device can be calculated. The moving coordinates (x, y) of the moving object are: x = x0 + Δx = x0 + d×cosθ, y = y0 + Δy = y0 + d×sinθ.

[0074] In an actual complex three-dimensional space scenario, such as corridor monitoring in a multi-story building, the information of the height dimension can also be considered. Assuming that the installation height of the radar device is z0, the height information of the target object can also be obtained through radar technology (for example, some radars can detect the vertical angle of the target object and calculate the height in combination with the distance information). Let the height difference of the target object relative to the radar device be Δz. Then the coordinates of the moving object in the three-dimensional space are (x, y, z), where the calculation methods of x and y are as in the above two-dimensional case, and z = z0 + Δz.

[0075] Step B3, construct a corresponding wake-up instruction using the moving coordinates and send the wake-up instruction to the visualization device.

[0076] Specifically, when constructing the wake-up instruction, the moving coordinate information of the determined moving object needs to be encapsulated in a specific format that the visualization device can recognize. For example, construct the wake-up instruction in JSON format. After the wake-up instruction is constructed, it is sent to the visualization device through the wireless link (such as Bluetooth connection) between the discrete radar device and the visualization device. The Bluetooth module, as the medium for data transmission, sends the encapsulated wake-up instruction in the form of a data packet according to the Bluetooth communication protocol. After the Bluetooth receiving module at the visualization device end receives the data packet, it parses it to extract key information such as the moving coordinates, and then triggers subsequent preset capture mechanisms, such as adjusting the camera focus and turning to the corresponding aiming area and other operations.

[0077] Step S13, monitor the environmental vision within the second preset range through the preset capture mechanism of the visualization device.

[0078] In the embodiment of the present application, step S13 includes the following steps C1 - C3:

[0079] Step C1, parse the moving coordinates in the wake-up instruction.

[0080] Specifically, after receiving the wake-up instruction sent by the separate radar device, the primary task of the visualization device is to parse the instruction content. Since the wake-up instruction contains key information such as the movement coordinates of the active object, the visualization device needs to extract this data according to a specific format protocol. For example, if the wake-up instruction is encapsulated in JSON format, the parsing program in the visualization device will read the JSON data and extract the x, y (and z, if applicable) coordinate values of the active object from the corresponding fields through specific functions or algorithms. Based on this, the visualization device performs subsequent operations such as adjusting the camera focal length and turning to the corresponding area.

[0081] Step C2: Adjust the camera focal length of the visualization device according to the movement coordinates and turn the visualization device to the aiming area corresponding to the movement coordinates.

[0082] Specifically, after successfully parsing the movement coordinates of the active object, the visualization device first needs to make corresponding adjustments to the camera according to this coordinate information. The adjustment of the camera focal length aims to make the active object clearly presented in the picture. For example, if the active object is far from the visualization device, in order to obtain a clear image, it is necessary to increase the camera focal length to pull the distant active object closer to the picture and form a clear image; conversely, if the active object is close, the focal length is appropriately reduced to avoid the over-amplification and distortion of the active object in the picture. Usually, the camera of the visualization device is equipped with an electric zoom function, and the control circuit inside the device will automatically calculate and output a control signal according to the parsed movement coordinates and the preset distance-focal length correspondence relationship, driving the zoom motor of the camera to act to achieve precise adjustment of the focal length.

[0083] Determine the turning angle of the visualization device according to the movement coordinates so that the camera can be aimed at the area where the active object is located. The visualization device generally uses a pan-tilt structure to achieve the turning function. The pan-tilt motor receives the control signal calculated by the device according to the movement coordinates and drives the camera to rotate in the horizontal and vertical directions. For example, assuming a coordinate system is established with the installation position of the visualization device as the coordinate origin, and the parsed movement coordinates of the active object are (x, y), the device calculates the angle α that the pan-tilt needs to rotate in the horizontal direction and the angle β that needs to rotate in the vertical direction through calculation methods such as trigonometric functions, and then controls the pan-tilt motor to rotate the corresponding angle to turn the camera to the aiming area corresponding to the movement coordinates to ensure that the active object is at the center of the camera's field of view or within the effective monitoring range.

[0084] Step C3: Extract the target contour of the active object in the environmental field of view. If the target contour conforms to the human contour, it is determined that the active object meets the preset conditions.

[0085] Specifically, after the visualization device completes the focal length adjustment and steering operations, the camera starts to collect image data of the environmental view. Next, the collected images need to be processed to extract the target contour of the moving object and determine whether it meets the preset conditions. Image processing algorithms are used to analyze the collected images. Common algorithms include edge detection algorithms (such as the Canny algorithm). This algorithm calculates the gradient values and directions of pixel points in the image to identify areas with obvious changes in gray values in the image, thereby outlining the edge contours of objects. For complex scene images, image segmentation techniques may also be required, such as threshold-based segmentation, region-growing-based segmentation, etc., to separate the moving object from the background and further accurately extract its contour. For example, in an image containing a person and the surrounding environment, the contour shape of the person can be accurately extracted through edge detection and image segmentation algorithms.

[0086] After the target contour is extracted, it is compared with a pre-set human body contour model or feature library. The template matching algorithm can be used to compare the extracted target contour with multiple standard human body contour templates stored in the device one by one and calculate the similarity between them. If the similarity exceeds a certain threshold, the target contour is considered to conform to the human body contour. Machine learning or deep learning models, such as convolutional neural networks (CNNs), can also be used to classify and identify the target contour. A large number of image data containing human and non-human contours are used to train the CNN model in advance so that it learns the characteristic patterns of human body contours. When the image of the extracted target contour is input, the CNN model can output the probability that the contour belongs to the human body contour. If the probability is greater than the set threshold, it can be determined that the moving object meets the preset conditions, and the visualization device will trigger subsequent capture operations to record relevant information about the moving object.

[0087] Step S14: Perform a capture operation on the moving object in the environmental view that meets the preset conditions to obtain a target capture image.

[0088] In the embodiment of the present application, first, after the control circuit inside the visualization device confirms that the active object meets the preset conditions, it generates and sends a capture instruction. This instruction activates the capture function of the camera module. For example, in the hardware architecture of the device, the control circuit sends a high-level or low-level signal to the driver chip of the camera module through a specific communication line, and the change of this signal is recognized by the driver chip as a capture instruction. After the capture instruction is issued, the camera module will adjust its own parameter settings according to the current shooting requirements. This includes adjusting the aperture size to control the amount of light entering, reasonably setting the aperture value according to the intensity of the ambient light to ensure that the brightness of the captured image is appropriate. At the same time, adjust the shutter speed. If the active object is in a fast-moving state, in order to avoid image blurring, the shutter opening time will be shortened so that light enters the camera for imaging in a very short time; on the contrary, when the light is relatively dim and the active object moves slowly, the shutter speed will be appropriately extended to obtain sufficient light. In addition, the sensitivity (ISO) will also be set. In a low-light environment, the sensitivity will be appropriately increased to enhance the brightness of the image, but it should be noted to avoid introducing too much noise due to too high sensitivity setting, which affects the image quality.

[0089] Secondly, after the camera module completes the parameter configuration, it starts the image acquisition process. The image sensor of the camera, such as a common CMOS (Complementary Metal Oxide Semiconductor) sensor, converts the received light signal into an electrical signal. The pixel points on the sensor generate corresponding electrical signal intensities according to the received light intensity. For example, in areas with stronger light, the electrical signal intensities generated by the pixel points are higher; while in areas with darker light, the electrical signal intensities are lower. These electrical signals are then transmitted to the image processing module for subsequent processing.

[0090] Thirdly, the image processing module performs preliminary processing on the received raw image data to improve the image quality and meet the storage and transmission requirements of the system. The processing process includes removing noise in the image. Noise is usually generated due to factors such as the electronic noise of the sensor or environmental interference. Through specific filtering algorithms, such as mean filtering, median filtering, etc., the noise in the image is smoothed to make the image clearer. At the same time, image enhancement operations are performed. For example, by adjusting the contrast and brightness of the image, the features of the active object are highlighted, making details such as the human outline and facial expressions more obvious, facilitating subsequent analysis and recognition.

[0091] Finally, the image data after preliminary processing is further encapsulated and stored to generate the target captured image. The visualization device encodes and compresses these images in a certain file format, such as the common JPEG (Joint Photographic Experts Group) format, to reduce the size of the image file for easy storage and transmission. The compressed image data is stored in the internal storage medium of the device, such as a flash chip. At the same time, according to the system settings, these images may also be uploaded to the cloud server via the WIFI module for backup storage. For example, in a home environment, users can download these captured images from the cloud server at any time through the mobile phone APP to view the activities in front of the door. The whole process ensures that the moving objects meeting the preset conditions can be accurately and timely captured, and high-quality target captured images are saved, providing important data support for subsequent applications such as security monitoring and event tracing.

[0092] In the embodiment of the present application, the method further includes steps D1 - D3:

[0093] Step D1, deploy at least two separate radar devices at different spatial positions, where each separate radar device independently detects moving objects within a preset range.

[0094] Specifically, in a complex actual scenario, the monitoring range and angle of a single radar have limitations. To achieve more comprehensive and accurate monitoring, it is necessary to deploy multiple separate radar devices. For example, in a long corridor house type, two radar devices can be installed at both ends of the long corridor respectively, so that their monitoring ranges can cover the entire long corridor area; if it is a corner house type, the radars can be installed at appropriate positions on both sides of the corner to ensure that moving objects approaching from different directions can be monitored. Each separate radar device has the ability to work independently, and they detect the preset range according to their own set parameters. These radar devices use technologies such as infrared or microwave radar to continuously scan the areas they are responsible for. For example, a microwave radar uses the emission and reception of microwave signals to detect targets. When an object moves within its monitoring range, it will cause changes in the reflected signal. The radar device analyzes these changes to identify moving objects, and the radars do not interfere with each other and independently complete the detection task of moving objects within their respective preset ranges.

[0095] Step D2, when multiple separate radar devices detect the moving trajectories of the same moving object, fuse the multiple moving trajectories to obtain a three-dimensional motion path.

[0096] Specifically, when multiple separate radar devices all detect the same moving object, due to the different installation positions of the radars, the obtained moving trajectory information of this moving object also varies. To more accurately grasp the true motion path of the moving object, it is necessary to fuse these different moving trajectories.

[0097] As an example, in a three-dimensional space scenario, radar A, radar B, and radar C all detect the movement of the same person. Radar A calculates a series of position coordinates of the person based on its own detection data to form a movement trajectory; radar B and radar C also obtain their respective corresponding trajectories. Through specific algorithms, such as those based on the principle of triangulation or multi-sensor data fusion algorithms, the trajectory data from these different sources are integrated. First, determine the coordinate positions of each radar device in space, and then combine the distance and direction information between the detected moving object and itself. Through mathematical calculations, the accurate position of the moving object in the three-dimensional space is obtained. Connecting these accurate position points at multiple moments can obtain the complete three-dimensional movement path of the moving object. This process is similar to shooting the movement of the same object with multiple cameras at different angles and then processing these video data to restore the movement trajectory of the object in the real three-dimensional space, so as to more comprehensively and accurately understand the movement of the moving object.

[0098] Step D3: Predict the temporal characteristics of the moving object approaching the visualization device in space based on the three-dimensional movement path, and adjust the aiming area of the visualization device according to the temporal characteristics.

[0099] Specifically, after obtaining the three-dimensional movement path of the moving object, its future movement trend can be predicted, especially the temporal characteristics of its approaching the visualization device. This requires the assistance of some data analysis and prediction algorithms, such as prediction methods based on time series analysis or prediction models in machine learning.

[0100] For example, by analyzing information such as the speed and acceleration of the moving object on the three-dimensional movement path in the past period of time, combined with its movement direction, predict the possible positions of the moving object at different future time points and the approximate time to reach a position closer to the visualization device. According to these predicted temporal characteristics, the aiming area of the visualization device is adjusted in advance. If it is predicted that the moving object will approach the visualization device from a certain direction within the next few seconds, the system will control the visualization device to turn the camera to that direction in advance and adjust the focal length to make that direction the current aiming area, ensuring that when the moving object enters the effective monitoring range of the visualization device, it can be captured in a timely and clear manner. Such an operation can improve the accuracy and timeliness of the visualization device's capture, avoid missing key capture images due to untimely response, and further enhance the monitoring and capture performance of the entire low-power cat-eye system in complex scenarios.

[0101] In the embodiment of the present application, the method further includes steps E1 - E3:

[0102] Step E1: When multiple separated radar devices detect different moving objects, control the separated radar devices to send multi-target position information to the visualization device.

[0103] Specifically, multiple separate radar devices may simultaneously detect multiple different moving objects. For example, in the common area of an apartment building, one radar may detect someone coming from the direction of the stairs, while another radar detects someone lingering at the elevator entrance. At this time, each radar device that detects a moving object needs to send the detected target position information to the visualization device. The radar device establishes a communication link with the visualization device through its built-in Bluetooth module. Taking Bluetooth communication as an example, the radar device packs the position information of the moving object according to a specific data format, which may include information such as the distance and direction of the target object relative to the radar device and the identification of the radar device itself. Multiple radar devices each send such packed multi-target position information to the visualization device in the form of data packets through the Bluetooth communication protocol. The Bluetooth receiving module of the visualization device is responsible for receiving these data packets and performing a preliminary analysis on them to extract the position-related information of each moving object, preparing for subsequent operations such as generating a dynamic warning area.

[0104] Step E2: Generate a dynamic warning area based on the multi-target position information and calculate the distance weight of each moving object from the visualization device.

[0105] Specifically, after receiving the multi-target position information, the visualization device first generates a dynamic warning area based on this information. This dynamic warning area is not fixed but will be adjusted in real time as the positions of the moving objects change. For example, if three moving objects are detected, located in different directions and at different distances, the visualization device will take itself as the center and, in combination with the position information of these moving objects, generate a warning area that includes these targets through a specific algorithm. This area may be an irregular polygon, and its shape and scope will change dynamically according to the distribution of the moving objects. At the same time, the visualization device needs to calculate the distance weight of each moving object from itself. The calculation of the distance weight is usually based on the actual distance between the moving object and the visualization device. The closer the moving object is to the visualization device, the higher its distance weight; the farther the moving object is, the lower the distance weight. The formula for calculating the distance weight can use a simple inverse proportion relationship:

[0106]

[0107] where W is the distance weight, d is the distance between the moving object and the visualization device, and ε is a very small constant used to avoid a zero denominator.

[0108] Through such a calculation method, the importance of each moving object to the visualization device can be quantified, so as to perform priority allocation subsequently.

[0109] Step E3: Assign corresponding priorities to the active objects based on the distance weights, configure the corresponding sampling rates according to the priorities, and control the visualization device to perform capture operations according to the sampling rates.

[0110] Specifically, according to the calculated distance weights, assign corresponding priorities to each active object. Active objects with high distance weights (i.e., close to the visualization device) are given higher priorities because these objects may be more critical to the security or monitoring requirements of the current scene. For example, an active object that is only 2 meters away from the visualization device will have a higher priority than an object that is 10 meters away. Configure the corresponding sampling rates for each active object. Active objects with high priorities have higher sampling rates set, which means the visualization device will capture them more frequently to obtain more image information; active objects with low priorities have relatively lower sampling rates. For example, the sampling rate of a high-priority active object may be set to capture 5 times per second, while the sampling rate of a low-priority active object may be set to capture 1 time every 5 seconds.

[0111] Subsequently, the visualization device performs capture operations on active objects with different priorities according to the configured sampling rates. During the capture process, the control circuit inside the device will trigger the camera module to collect images at regular intervals according to the set sampling rate, and the collected image data will be preliminarily processed by the image processing module, and then uploaded to the cloud server for storage through the Bluetooth or WiFi module, so as to achieve targeted monitoring and recording of different active objects according to their importance, and improve the monitoring efficiency and resource utilization efficiency of the entire low-power cat eye system in a multi-target scenario.

[0112] In the embodiment of the present application, the method further includes steps F1 - F3:

[0113] Step F1: Receive the installation location information of each separate radar device input by the user and the first preset range.

[0114] Specifically, during the setup phase of the low-power cat eye system, the user needs to interact with the system and input the key information of each separate radar device. The user can complete this operation through the supporting mobile APP or other user interfaces.

[0115] The user accurately marks the installation location of each radar device on the map interface or dedicated input area of the APP. For example, in a home scenario, the user can select the map area of the room where the door is located in the APP, and then place the icon of the radar device at the actual installation location, such as the corner above the door or the wall near the window, by clicking or dragging the icon. At the same time, the user can also supplement detailed information through text description, such as "installed 20 centimeters below the ceiling above the living room door".

[0116] In addition to the installation location, the user also needs to set the first preset range for each radar device. This range determines the size of the area where the radar device can effectively detect moving objects. The user can adjust it according to actual needs and scene characteristics. For example, in an open area in front of a door, the user may set the preset range of a certain radar to a circular area with a radius of 5 meters centered on the installation point; while in a narrow corridor environment, it may be set to a rectangular area 3 meters long and 1 meter wide in front of the radar centered on the radar. The APP provides corresponding setting tools such as sliders and input boxes to facilitate the user to input specific values to determine the size and shape parameters of the preset range. After the system receives this user input information, it stores it in the local database or a storage device associated with the system for subsequent use.

[0117] Step F2: Bind the installation location information and the first preset range to the visualization device for identification to generate a topological mapping relationship.

[0118] Specifically, after receiving the installation location information and the first preset range of the split radar device input by the user, an identification binding operation will be performed to establish the association between this information and the visualization device and generate a topological mapping relationship. Assign a unique identifier to each split radar device. This identifier can be a numerical number, a code combination of letters and numbers, etc. For example, the first installed radar device is identified as "R001", the second as "R002", and so on. At the same time, the visualization device also has its own unique identifier, such as "V001". Integrate the identifier of each radar device with its corresponding installation location information and the first preset range information, and associate it with the identifier of the visualization device. For example, create a record in the database, and the record content includes the radar device identifier "R001", the coordinate information of the installation location (assuming the longitude and latitude coordinates obtained through map positioning), the shape and size parameters of the first preset range (such as a circular radius of 5 meters), and the associated visualization device identifier "V001". In this way, the corresponding relationship between the radar device and the visualization device, that is, the topological mapping relationship, is established. This mapping relationship enables the system to clearly understand the position of each radar device in space, the monitoring range, and which visualization device it is associated with, providing a basis for subsequent collaborative work and data processing.

[0119] Step F3: Dynamically adjust the scanning frequencies of the respective split radar devices according to the topological mapping relationship.

[0120] Specifically, based on the generated topological mapping relationship, the scanning frequencies of each separate radar device are dynamically adjusted according to the actual situation to optimize the system performance and resource utilization efficiency. When it is determined that the activity in a certain area is relatively frequent or important, the scanning frequency of the radar device in that area will be increased. For example, if the topological mapping relationship shows that a certain radar device is installed near the main passage where people often come in and out, and according to historical data or current monitoring requirements, this area requires more frequent monitoring, the scanning frequency of this radar device will be automatically increased. On the contrary, if there is less activity in a certain area, the scanning frequency of the corresponding radar device will be reduced to save energy. Through the communication link with the radar device, an instruction is sent to adjust its scanning frequency. After receiving the instruction, the radar device adjusts the internal scanning parameters according to the instruction content. For example, for a microwave radar device, the adjustment of its scanning frequency involves adjusting the frequency switching period of the transmitted signal. If a full-range scan was originally performed every 10 seconds, after receiving the instruction to increase the scanning frequency, the scanning period is shortened to 5 seconds. By dynamically adjusting the scanning frequency in this way, on the premise of ensuring effective monitoring, resources are reasonably allocated, the overall power consumption is reduced, and the operating efficiency and adaptability of the entire low-power cat-eye system are improved.

[0121] In the embodiment of the present application, the assembly scheme of the separate radar device may further include:

[0122] The separate radar device adopts a modular magnetic adsorption assembly structure. Each radar module is designed as an independent unit, externally wrapped with a rubber shell with high strength, wear resistance and a certain degree of flexibility, which not only plays a protective role, but also can buffer collisions to a certain extent. A strong magnet is embedded on one side of the module, and a magnetic adsorption metal plate matching the magnet is on the other side. This design enables multiple radar modules to be easily spliced and combined. Users can adsorb different numbers of radar modules together according to actual monitoring needs to form a larger radar monitoring area.

[0123] To achieve multi-angle monitoring of the radar device, a special multi-angle adjustable bracket is equipped. The bracket is made of high-strength aluminum alloy and has good corrosion resistance and load-bearing capacity. One end of the bracket is connected to the radar module through a universal joint, and the universal joint can achieve 360-degree horizontal rotation and 180-degree vertical pitch. Users can easily adjust the angle of the radar module according to the on-site environment and the position of the monitoring target. The other end of the bracket is designed with a variety of installation interfaces, including screw holes, suction cups and strong adhesive stickers, etc. For walls that can be drilled, users can use screws to fix the bracket; for glass or ceramic tile surfaces that are not suitable for drilling, users can choose to use suction cups or strong adhesive stickers for installation, improving the flexibility of assembly.

[0124] In some scenarios that require flexible adjustment of the radar monitoring position, such as large warehouses or exhibition halls, an orbital sliding assembly system is introduced. Specialized tracks are installed on the ceiling or wall. The tracks are composed of high-strength plastic and metal guide rails, with good smoothness and stability. The radar module is connected to the track through a sliding device. The sliding device is equipped with a micro motor and gears inside, and can automatically slide along the track. Users can remotely control the moving speed and position of the radar module on the track through the supporting mobile phone APP or control panel. At the same time, multiple positioning points are set on the track, and the radar module can automatically stop at these positioning points and conduct precise monitoring. For example, during warehouse inventory, users can control the radar module to scan each shelf area along the track in sequence to ensure the safety of goods and the accuracy of inventory.

[0125] To ensure the best assembly effect of the radar device, an intelligent adaptive assembly feedback mechanism can also be introduced. After the radar module is assembled, self-check is automatically carried out. The radar module will emit a series of test signals and judge whether its installation angle and position are appropriate according to the reflection of the signals. If it is found that the signals are blocked or the monitoring range does not meet the expectation, the system will send a prompt to the user through the mobile phone APP and provide adjustment suggestions. For example, if the installation angle of the radar module is too low, resulting in some monitoring areas not being covered, the user will be prompted to adjust the radar module upward by a certain angle. At the same time, the relevant data of each assembly will be recorded, including the installation position, angle and monitoring effect, etc., for subsequent analysis and optimization.

[0126] Through the above assembly scheme of the split radar device, it is possible to meet the installation requirements of different users in various complex environments, improve the installation efficiency and monitoring effect of the radar device, and provide stronger support for the entire environment simulation and safety monitoring system.

[0127] In this embodiment, a capture device for a split visualization device is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0128] This embodiment provides a capture device for a split visualization device, as Figure 5 shown, including:

[0129] A detection module 51, configured to detect moving objects within a first preset range through a split radar device when the visualization device is in a sleep state, wherein the split radar device is connected to the visualization device through a wireless link;

[0130] A sending module 52, configured to send a wake-up instruction to a visualization device when an active object is detected by a split radar device, so that the visualization device triggers a preset capture mechanism according to the wake-up instruction;

[0131] A monitoring module 53, configured to monitor the environmental field of view within a second preset range through the preset capture mechanism of the visualization device;

[0132] An execution module 54, configured to perform a capture operation on an active object that meets the preset conditions in the environmental field of view to obtain a target capture image.

[0133] Further, a detection module 51 is configured to detect a target object within a first preset range by using a split radar device, and calculate the real-time distance and movement direction between the target object and the split radar device; according to the real-time distance and movement direction, determine whether the movement trajectory of the target object meets the preset movement trajectory condition; if the movement trajectory of the target object meets the preset movement trajectory condition, determine that there is an active object within the first preset range.

[0134] Further, the sending module 52 is configured to obtain the installation position of the split radar device; determine the movement coordinates of the active object according to the installation position and the real-time distance; construct a corresponding wake-up instruction by using the movement coordinates, and send the wake-up instruction to the visualization device.

[0135] Further, the monitoring module 53 is configured to parse the movement coordinates in the wake-up instruction; adjust the camera focal length of the visualization device according to the movement coordinates, and turn the visualization device to the aiming area corresponding to the movement coordinates; extract the target contour of the active object in the environmental field of view, and if the target contour conforms to the human contour, determine that the active object meets the preset conditions.

[0136] Further, the device further includes: a prediction module, configured to deploy at least two split radar devices at different spatial positions, wherein each split radar device independently detects an active object within a preset range; when multiple split radar devices detect the movement trajectories of the same active object, fuse the multiple movement trajectories to obtain a three-dimensional movement path; predict the timing characteristics of the active object approaching the visualization device in space according to the three-dimensional movement path, and adjust the aiming area of the visualization device according to the timing characteristics.

[0137] Further, the device further includes: a calculation module, configured to, when multiple split radar devices detect different active objects, control the split radar devices to send multi-target position information to the visualization device; generate a dynamic warning area according to the multi-target position information, and calculate the distance weight between each active object and the visualization device; assign corresponding priorities to the active objects based on the distance weights, and configure corresponding sampling rates according to the priorities, and control the visualization device to perform capture operations according to the sampling rates.

[0138] Further, the apparatus further includes: an adjustment module, configured to receive installation position information of each separate radar device input by a user and a first preset range; bind the installation position information and the first preset range with the visualization device for identification to generate a topological mapping relationship; and dynamically adjust the scanning frequency of each separate radar device according to the topological mapping relationship.

[0139] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As Figure 6 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common main board or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system).

[0140] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0141] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0142] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of a computer device for the display of a kind of mini-program landing page, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0143] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above-mentioned types of memory.

[0144] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0145] Embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention may be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and to be downloaded through a network and stored in a local storage medium, so that the methods described herein can be stored in such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0146] Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A capture method for a separable visualization device, characterized in that, The method includes: When the visualization device is in a sleep state, detecting active objects within a first preset range through a separate radar device, where the separate radar device is connected to the visualization device through a wireless link; When the separate radar device detects an active object, sending a wake-up instruction to the visualization device so that the visualization device triggers a preset capture mechanism according to the wake-up instruction; Monitoring the environmental view within a second preset range through the preset capture mechanism of the visualization device; Performing a capture operation on the active objects in the environmental view that meet the preset conditions to obtain a target capture image.

2. The method according to claim 1, characterized in that The detecting of active objects within a first preset range through the separate radar device includes: Using the separate radar device to detect a target object within a first preset range and calculating the real-time distance and movement direction between the target object and the separate radar device; Judging whether the movement trajectory of the target object meets the preset movement trajectory conditions according to the real-time distance and the movement direction; If the movement trajectory of the target object meets the preset movement trajectory conditions, determining that there are active objects within the first preset range.

3. The method according to claim 1, characterized in that The sending of the wake-up instruction to the visualization device includes: Obtaining the installation position of the separate radar device; Determining the movement coordinates of the active object according to the installation position and the real-time distance; Constructing a corresponding wake-up instruction using the movement coordinates and sending the wake-up instruction to the visualization device.

4. The method according to claim 1, characterized in that, The monitoring of the environmental view within a second preset range through the preset capture mechanism of the visualization device includes: Analyzing the movement coordinates in the wake-up instruction; Adjusting the camera focal length of the visualization device according to the movement coordinates and turning the visualization device to the aiming area corresponding to the movement coordinates; Extracting the target contour of the active object in the environmental view, and if the target contour conforms to the human contour, determining that the active object meets the preset conditions.

5. The method according to claim 4, wherein The method further includes: Deploying at least two separate radar devices at different spatial positions, where each separate radar device independently detects active objects within a preset range; When multiple separate radar devices detect the movement trajectories of the same active object, fusing the multiple movement trajectories to obtain a three-dimensional movement path; Predicting the timing characteristics of the active object approaching the visualization device in space according to the three-dimensional movement path and adjusting the aiming area of the visualization device according to the timing characteristics.

6. The method according to claim 5, characterized in that, The method further includes: When multiple separate radar devices detect different active objects, controlling the separate radar devices to send multi-target position information to the visualization device; Generating a dynamic warning area according to the multi-target position information and calculating the distance weight of each active object from the visualization device; Assigning corresponding priorities to the active objects based on the distance weights and configuring corresponding sampling rates according to the priorities, and controlling the visualization device to perform capture operations according to the sampling rates.

7. The method according to claim 1, characterized in that The method further includes: Receiving the installation position information of each separate radar device and the first preset range input by the user; Bind the installation location information and the first preset range to the visualization device for identification to generate a topological mapping relationship; Dynamically adjust the scanning frequencies of the respective split radar devices according to the topological mapping relationship.

8. A capture device for a separable visualization device, characterized in that, The device includes: A detection module, configured to detect moving objects within a first preset range through a split radar device when the visualization device is in a sleep state, wherein the split radar device is connected to the visualization device through a wireless link; A sending module, configured to send a wake-up instruction to the visualization device when the split radar device detects a moving object, so that the visualization device triggers a preset capture mechanism according to the wake-up instruction; A monitoring module, configured to monitor the environmental view within a second preset range through the preset capture mechanism of the visualization device; An execution module, configured to perform a capture operation on the moving objects in the environmental view that meet the preset conditions to obtain a target capture image.

9. A computer device, characterized in that, Includes: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

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