CN114973564A - A method and device for detecting remote personnel intrusion under no-light conditions - Google Patents

Abstract

The disclosure relates to a method and a device for detecting remote personnel intrusion under non-illumination conditions, electronic equipment and a storage medium. Wherein, the method comprises the following steps: acquiring environmental data based on a laser radar, and generating laser radar data; analyzing the laser radar data, presetting a first threshold value based on the size of an invader, judging and generating a camera control signal; operating the holder of the camera to rotate, focusing the focal length of the camera to the corresponding position in the position information, collecting the image at the position, and generating an image signal; and carrying out invader identification processing on the image signal, judging the characteristics of the invader and generating an alarm signal. According to the method, the laser radar and the camera are combined to control monitoring, the detection and monitoring of the over-distance target are achieved, the influence of external environment factors is small, and the detection performance and the application scene are greatly improved.

Description

A method and device for detecting remote personnel intrusion under no-light conditions

technical field

The present disclosure relates to the field of security monitoring, and in particular, to a method, device, electronic device, and computer-readable storage medium for detecting remote personnel intrusion under no-light conditions.

Background technique

In current security applications, a camera device and an image recognition technology are usually used to perform image recognition on the image of the area to be detected collected by the camera device, so as to determine whether there is an outsider or foreign object entering the area to be detected. However, usually the field of view that a camera can cover is less than 50 meters. At the same time, due to the interference of the external environment, such as light, rain and snow, some images captured by the camera device cannot be guaranteed to be clear, resulting in errors in image recognition, which cannot be detected in time. To the occurrence of regional intrusion, and if it is based on pure vision, if the intruder camouflages through other methods such as occlusion, the vision cannot determine that there is a human intrusion, resulting in missed reports.

Therefore, one or more methods are needed to solve the above problems.

It should be noted that the information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure, and therefore may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.

SUMMARY OF THE INVENTION

The purpose of the present disclosure is to provide a method, device, electronic device, and computer-readable storage medium for detecting remote personnel intrusion under no-light conditions, so as to at least to a certain extent overcome one or more of the limitations and defects of the related art. Multiple questions.

According to one aspect of the present disclosure, a method for detecting remote personnel intrusion under no-light conditions is provided, including:

Collect environmental data based on lidar, generate lidar data, and send the lidar data to the main controller;

After receiving the lidar data sent by the lidar, the main controller analyzes the lidar data, presets a first threshold based on the size of the intruder, determines and generates a camera control signal, and controls the camera to control the signal to the camera;

After the camera receives the camera control signal sent by the main controller, it analyzes and extracts the position information in the camera control signal, operates the pan-tilt rotation of the camera according to the camera control signal, and makes the camera focal length. Focusing on the corresponding position in the position information, collecting the image of the shown position, and generating an image signal;

Based on the image signal, an intruder identification process is performed, the characteristics of the intruder are judged, and an alarm signal is generated.

In an exemplary embodiment of the present disclosure, the method further includes:

Collect environmental data based on lidar, and generate lidar data as electric cloud lidar data.

In an exemplary embodiment of the present disclosure, the method further includes:

After receiving the laser radar data sent by the laser radar, the main controller analyzes the laser radar data, compares the laser radar data with the preset background data, generates foreign object intrusion data, and analyzes the foreign object. The intrusion data is compared with a preset first threshold of the size of the intruder to determine whether the foreign object intrusion data meets the intrusion standard.

In an exemplary embodiment of the present disclosure, the intruder judgment based on the lidar in the method further includes:

In the background learning step, when the lidar is installed and initialized, the environment is cleared, the preset frame point cloud is collected, and then the point cloud is stored in the octree structure, and the number of point clouds in each grid and the number of points appearing in each grid are counted. Probability, the grid with a probability greater than 80% is determined as a grid containing background points, otherwise it is determined that there are no points in the grid or the points are noise points;

In the background update step, when no foreign object is detected in the environment, the background update is performed every preset time period;

In the step of screening the point cloud of the intruder, the point in the real-time point cloud is searched for the point with the closest distance in the background octree structure. If the point corresponding to the point in the real-time point cloud is found within the preset distance threshold range, then Determine that the point in the real-time point cloud is the background, otherwise determine that the point in the real-time point cloud is the point of the intruder;

In the target clustering step, when it is determined that a point in the real-time point cloud is a point of an intruder, all points in the real-time point cloud are clustered based on Euclidean distance information, and the size of the clustered object is smaller than the size of the first cluster. If the class threshold is greater than the second clustering threshold, it is determined as a noise point, otherwise it is determined as an intruder, and size and position information of the intruder is detected and generated.

In an exemplary embodiment of the present disclosure, the method further includes:

The position information of the camera coverage area is divided into a plurality of preset areas, and after receiving the camera control signal sent by the main controller, the camera analyzes and extracts the position information in the camera control signal, The pan/tilt of the camera is operated to rotate according to the camera control signal, and the focal length of the camera is focused to the corresponding preset area in the position information.

In an exemplary embodiment of the present disclosure, the method further includes:

Based on the YOLOV5 algorithm, the image signal is processed for intruder identification, the characteristics of the intruder are judged, and an alarm signal is generated.

In an exemplary embodiment of the present disclosure, the method further includes:

After receiving the lidar data sent by the lidar, the main controller analyzes the lidar data, presets a second threshold based on the size of the intruder, and generates an alarm signal.

In an exemplary embodiment of the present disclosure, the method further includes:

When it is determined that a point in the real-time point cloud is a point of an intruder, all points in the real-time point cloud are clustered based on the Euclidean distance information, and the size of the clustered object is greater than the third clustering threshold, and it is determined as The lidar is blocked by foreign objects and generates an alarm signal.

In an exemplary embodiment of the present disclosure, the method further includes:

After the alarm signal is generated, the alarm signal and the image signal corresponding to the alarm signal are sent to the remote terminal based on the communication module.

In one aspect of the present disclosure, a device for detecting remote personnel intrusion under no-light conditions is provided, including:

Lidar, for collecting environmental data, generating Lidar data, and sending the Lidar data to the main controller;

The main controller is used to analyze the lidar data after receiving the lidar data sent by the lidar, and preset a first threshold based on the size of the intruder, judge and generate a camera control signal, and use the The camera control signal is sent to the camera;

The camera is used for analyzing and extracting the position information in the camera control signal after receiving the camera control signal sent by the main controller, operating the pan-tilt of the camera to rotate according to the camera control signal, and making all The focal length of the camera is focused to the corresponding position in the position information, the image of the shown position is collected, an image signal is generated, the intruder identification processing is performed based on the image signal, the characteristics of the intruder are judged, and an alarm signal is generated.

In one aspect of the present disclosure, an electronic device is provided, comprising:

processor; and

and a memory, where computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by the processor, implement the method according to any one of the above.

In one aspect of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the method according to any one of the above.

In an exemplary embodiment of the present disclosure, a method for detecting long-distance personnel intrusion under no-light conditions, wherein the method includes: collecting environmental data based on lidar to generate lidar data; analyzing the lidar data, And preset a first threshold based on the size of the intruder, determine and generate a camera control signal; operate the pan-tilt of the camera to rotate, so that the camera focal length is focused to the corresponding position in the position information, and the image of the displayed position is performed. Collect and generate image signals; perform intruder identification processing on the image signals, judge the characteristics of the intruders and generate alarm signals. The present disclosure realizes the detection and monitoring of ultra-distance targets through the combined control and monitoring method of the laser radar and the camera, is less affected by external environmental factors, and greatly improves the detection performance and application scenarios.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

The above and other features and advantages of the present disclosure will become more apparent from the detailed description of example embodiments thereof with reference to the accompanying drawings.

FIG. 1 shows a flowchart of a method for detecting remote personnel intrusion under no-light conditions according to an exemplary embodiment of the present disclosure;

FIG. 2 shows a schematic diagram of an application scenario of a method for detecting remote personnel intrusion under no-light conditions according to an exemplary embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of an octogram algorithm of a method for detecting remote personnel intrusion under no-light conditions according to an exemplary embodiment of the present disclosure;

FIG. 4 shows a schematic block diagram of a device for detecting remote personnel intrusion under no-light conditions according to an exemplary embodiment of the present disclosure;

FIG. 5 schematically shows a block diagram of an electronic device according to an exemplary embodiment of the present disclosure; and

FIG. 6 schematically shows a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure.

Detailed ways

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments, however, can be embodied in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated descriptions will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of the embodiments of the present disclosure. However, one skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. may be employed. In other instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

The block diagrams shown in the figures are merely functional entities and do not necessarily necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, or in one or more software-hardened modules or parts of functional entities, or in different network and/or processor devices and/or microcontroller devices implement these functional entities.

In this exemplary embodiment, a method for detecting remote personnel intrusion under no-light conditions is first provided; with reference to FIG. 1 , the method for detecting long-distance personnel intrusion under no-light conditions may include the following steps:

Step S110, collect environmental data based on the lidar, generate lidar data, and send the lidar data to the main controller;

Step S120, after receiving the lidar data sent by the lidar, the main controller analyzes the lidar data, presets a first threshold based on the size of the intruder, determines and generates a camera control signal, and uses the The camera control signal is sent to the camera;

Step S130, after receiving the camera control signal sent by the main controller, the camera analyzes and extracts the position information in the camera control signal, operates the pan-tilt of the camera to rotate according to the camera control signal, and makes all the camera heads rotate. The focal length of the camera is focused to the corresponding position in the position information, and the image of the shown position is collected to generate an image signal;

In step S140, an intruder identification process is performed based on the image signal, the characteristics of the intruder are determined, and an alarm signal is generated.

In an exemplary embodiment of the present disclosure, a method for detecting long-distance personnel intrusion under no-light conditions, wherein the method includes: collecting environmental data based on lidar to generate lidar data; analyzing the lidar data, And preset a first threshold based on the size of the intruder, determine and generate a camera control signal; operate the pan-tilt of the camera to rotate, so that the camera focal length is focused to the corresponding position in the position information, and the image of the displayed position is performed. Collect and generate image signals; perform intruder identification processing on the image signals, judge the characteristics of the intruders and generate alarm signals. The present disclosure realizes the detection and monitoring of ultra-distance targets through the combined control and monitoring method of the laser radar and the camera, is less affected by external environmental factors, and greatly improves the detection performance and application scenarios.

Next, a method for detecting remote personnel intrusion under no-light conditions in this exemplary embodiment will be further described.

In the embodiment of this example, in order to solve the problems that personnel intrusion detection in substations, factories, high-speed railway stations, borders, etc. relies on illumination, the coverage is narrow, and the false alarm rate and the false alarm rate are high, the present invention proposes a Independent of lighting conditions, a single device can cover up to 150 meters, 7*24 hours unattended personnel intrusion detection method and system, the system can work normally in the daytime with light and night without light, without affecting the detection performance.

In step S110, the environment data may be collected based on the lidar, the lidar data may be generated, and the lidar data may be sent to the main controller.

In the embodiment of this example, the environment data is collected based on the lidar, and the generated lidar data is the electric cloud lidar data.

In the embodiment of this example, the lidar can actively emit laser beams to different angles in the three-dimensional space. Because it is actively emitting light, it does not rely on ambient lighting. After the laser beam encounters an object, it returns to the laser receiver through diffuse reflection. The module can calculate the distance and position information between the transmitter and the object according to the time interval between sending and receiving signals multiplied by the speed of light and then divided by 2. The error accuracy is less than ±2cm. The lidar sensor finally obtains some parts of the entire three-dimensional space. The precise points are called "point clouds".

In the embodiment of this example, the point cloud data of the lidar can scan objects in the entire three-dimensional space, and obtain precise size and position information. During the installation and initialization, the system records the current scene as the background. During the real-time operation, the current point cloud data is compared with the background data. The real-time data minus the background data is the foreign body intrusion data, and then obtained through the point cloud clustering algorithm. to the location and size of the invading foreign body. When the distance is relatively long (greater than 50 meters), the lidar detects that the image pixel of the intrusion point cloud is low, and the type of the intruder cannot be identified through the point cloud.

In the embodiment of this example, the lidar selects TELE-15, the detection distance is 500 meters, the distance accuracy is 2 cm, and the field of view angle is 14.5°*16.2° (different lidars can be adapted according to different needs); camera DS-2DC7423IW- AE, infrared night vision 150 meters, ball camera (gimbal), 4 million pixels, 23 times zoom.

In step S120, after receiving the lidar data sent by the lidar, the main controller may analyze the lidar data, and preset a first threshold based on the size of the intruder to determine and generate a camera control signal, The camera control signal is sent to the camera.

In the embodiment of this example, the method further includes:

After receiving the laser radar data sent by the laser radar, the main controller analyzes the laser radar data, compares the laser radar data with the preset background data, generates foreign object intrusion data, and analyzes the foreign object. The intrusion data is compared with a preset first threshold of the size of the intruder to determine whether the foreign object intrusion data meets the intrusion standard.

In the embodiment of this example, the intruder judgment based on the lidar in the method further includes:

In the background learning step, when the lidar is installed and initialized, the environment is cleared, the preset frame point cloud is collected, and then the point cloud is stored in the octree structure, and the number of point clouds in each grid and the number of points appearing in each grid are counted. Probability, the grid with a probability greater than 80% is determined as a grid containing background points, otherwise it is determined that there are no points in the grid or the points are noise points;

In the background update step, when no foreign object is detected in the environment, the background update is performed every preset time period;

In the step of screening the point cloud of the intruder, the point in the real-time point cloud is searched for the point with the closest distance in the background octree structure. If the point corresponding to the point in the real-time point cloud is found within the preset distance threshold range, then Determine that the point in the real-time point cloud is the background, otherwise determine that the point in the real-time point cloud is the point of the intruder;

In the target clustering step, when it is determined that a point in the real-time point cloud is a point of an intruder, all points in the real-time point cloud are clustered based on Euclidean distance information, and the size of the clustered object is smaller than the size of the first cluster. If the class threshold is greater than the second clustering threshold, it is determined as a noise point, otherwise it is determined as an intruder, and size and position information of the intruder is detected and generated.

In the embodiment of this example, the laser radar intrusion detection and foreign object intrusion detection algorithm includes three steps: background learning, background update, intrusion point cloud screening, and target clustering, which are described in detail as follows:

1) Background learning, when the device is installed and initialized, the environment is cleared, 50 frames of point clouds are collected, and then the point clouds are stored in the octree structure as shown in Figure 3, and the number of point clouds in each grid is counted If the probability is greater than 80%, it is considered that the grid has background points, otherwise it is considered that there are no points or noise in the grid.

2) Background update, when there are gradual changes in the environment, such as snowfall and tree growth, if the background update is not performed, objects that change normally in the environment will be recognized as intrusions, resulting in false positives. The background update method is to perform a background update every 2 hours, and the premise of performing the background update is that no foreign objects are detected in the environment.

3) Screening of the intruder point cloud, each point of the real-time point cloud is searched for the nearest point in the background octree structure, if the corresponding point is found within the distance threshold range, the point is considered to be the background, if it is within the threshold range If no suitable point is found, it is considered to be the point of the intruder.

4) Target clustering, after obtaining all the point clouds of the intruder, finally cluster all the point clouds according to the Euclidean distance information, the size of the clustered object is smaller than the threshold (such as 30cm*30cm) or larger than the threshold (such as 300cm* 100cm) is considered as noise, otherwise it is considered as an intrusion, and finally the size and position information of the intrusion is obtained.

In step S130, after receiving the camera control signal sent by the main controller, the camera may analyze and extract the position information in the camera control signal, and operate the pan/tilt of the camera to rotate according to the camera control signal, and make the focal length of the camera focus on the corresponding position in the position information, and collect the image of the shown position to generate an image signal.

In the embodiment of this example, the method further includes:

The position information of the camera coverage area is divided into a plurality of preset areas, and after receiving the camera control signal sent by the main controller, the camera analyzes and extracts the position information in the camera control signal, The pan/tilt of the camera is operated to rotate according to the camera control signal, and the focal length of the camera is focused to the corresponding preset area in the position information.

In the embodiment of this example, a premise of identifying a person or other object in the environment through a picture is that a relatively clear picture can be taken. If the camera is in a fixed posture, especially when there is no light at night, the clear picture area of the camera is very small, beyond this range, no clear photos can be taken, and no human intrusion can be detected in the environment. In the present invention, the intrusion position of foreign objects is detected in advance by the laser radar (independent of light), and then the pan/tilt and focal length of the camera are adjusted to the designated area through the obtained position information, and the YOLOV5 algorithm is used to identify the intrusion object after taking high-definition pictures. Type, determine whether it is a human intrusion or other object intrusion. In some scenarios, if the requirements for intrusion protection are high, the size of the point cloud can be used to determine whether it is an intruding foreign object. The camera mainly plays a monitoring role and assists the staff to identify the type of intrusion.

In the embodiment of this example, the joint calibration method of the lidar and the camera is, as shown in FIG. 2 , the detection coverage area of the lidar is 10-150 meters, and the detection result contains accurate position information and size information. The coverage area of a picture of the camera is about 20 meters, and the coverage area of the lidar can be divided into 7 areas, each area corresponds to a preset point of the camera head, and the main controller can control each preset point of the camera through commands. For example, if the lidar detects a foreign body intrusion at a distance of 35 meters, corresponding to preset point 2 of the camera, the main controller sends the corresponding command to the camera and the camera can automatically set the focus and focal length to this area to achieve high-definition images. Photograph.

In step S140, an intruder identification process may be performed based on the image signal to determine the characteristics of the intruder and generate an alarm signal.

In the embodiment of this example, the method further includes:

Based on the YOLOV5 algorithm, the image signal is processed for intruder identification, the characteristics of the intruder are judged, and an alarm signal is generated.

In the embodiment of this example, the method further includes:

After receiving the lidar data sent by the lidar, the main controller analyzes the lidar data, presets a second threshold based on the size of the intruder, and generates an alarm signal.

In the embodiment of this example, the method further includes:

When it is determined that a point in the real-time point cloud is a point of an intruder, all points in the real-time point cloud are clustered based on the Euclidean distance information, and the size of the clustered object is greater than the third clustering threshold, and it is determined as The lidar is blocked by foreign objects and generates an alarm signal.

In the embodiment of this example, when the lidar mirror is blocked by a foreign object, a large difference is found by comparing with the background, and a warning message that the lidar is blocked will be sent to the system.

In the embodiment of this example, the method further includes:

After the alarm signal is generated, the alarm signal and the image signal corresponding to the alarm signal are sent to the remote terminal based on the communication module.

In the embodiment of this example, the data communication system supports two communication modes of optical fiber and 4G, and the data transmission adopts the mode of TCP.

In the embodiment of this example, the remote video system supports two ways to view video: The first is the traffic-saving mode. Under the condition of only 4G communication, in order to save traffic, the front-end pushes the JPG format to the background at a frame rate of 10FPS. The second, normal mode, pushes the video stream directly to the background by means of streaming. You can also remotely control the camera pan/tilt in the background to view the real-time situation of the entire protection area.

It should be noted that although the various steps of the methods of the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order, or that all illustrated steps must be performed in order to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step for execution, and/or one step may be decomposed into multiple steps for execution, and the like.

In addition, in this exemplary embodiment, a device for detecting remote personnel intrusion under no-light conditions is also provided. Referring to FIG. 4 , the device 400 for detecting remote personnel intrusion under no-light conditions may include: a lidar 410 , a main controller 420 and a camera 430 .

in:

Lidar 410, for collecting environmental data, generating Lidar data, and sending the Lidar data to the main controller;

The main controller 420 is configured to analyze the lidar data after receiving the lidar data sent by the lidar, and preset a first threshold based on the size of the intruder, determine and generate a camera control signal, the camera control signal is sent to the camera;

The camera 430 is configured to analyze and extract the position information in the camera control signal after receiving the camera control signal sent by the main controller, operate the pan/tilt of the camera to rotate according to the camera control signal, and make the camera rotate. The focal length of the camera is focused to the corresponding position in the position information, the image of the shown position is collected, an image signal is generated, the intruder identification processing is performed based on the image signal, the characteristics of the intruder are judged, and an alarm signal is generated.

The specific details of each of the above-mentioned remote personnel intrusion detection device modules under no-light conditions have been described in detail in the corresponding method for long-distance personnel intrusion detection under no-light conditions, so they will not be repeated here. .

It should be noted that although several modules or units of a remote person intrusion detection device 400 under no-light conditions are mentioned in the above detailed description, such division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided into multiple modules or units to be embodied.

In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

As will be appreciated by one skilled in the art, various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention can be embodied in the following forms, namely: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which may be collectively referred to herein as "circuit", "module" or "system".

An electronic device 500 according to such an embodiment of the present invention is described below with reference to FIG. 5 . The electronic device 500 shown in FIG. 5 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present invention.

As shown in FIG. 5, electronic device 500 takes the form of a general-purpose computing device. Components of the electronic device 500 may include, but are not limited to, the above-mentioned at least one processing unit 510 , the above-mentioned at least one storage unit 520 , a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510 ), and a display unit 540 .

Wherein, the storage unit stores program codes, and the program codes can be executed by the processing unit 510, so that the processing unit 510 executes various exemplary methods according to the present invention described in the above-mentioned “Exemplary Methods” section of this specification Example steps. For example, the processing unit 510 may perform steps S110 to S140 as shown in FIG. 1 .

The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 5201 and/or a cache storage unit 5202 , and may further include a read only storage unit (ROM) 5203 .

The storage unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5203, such program modules 5205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, An implementation of a network environment may be included in each or some combination of these examples.

The bus 550 may be representative of one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures. bus.

The electronic device 500 may also communicate with one or more external devices 570 (eg, keyboards, pointing devices, Bluetooth devices, etc.), with one or more devices that enable a user to interact with the electronic device 500, and/or with Any device (eg, router, modem, etc.) that enables the electronic device 500 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 550 . Also, the electronic device 500 may communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through a network adapter 560 . As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 550 . It should be understood that, although not shown, other hardware and/or software modules may be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives and data backup storage systems.

From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein may be implemented by software, or may be implemented by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of software products, and the software products may be stored in a non-volatile storage medium (which may be CD-ROM, U disk, mobile hard disk, etc.) or on a network , including several instructions to cause a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium on which a program product capable of implementing the above-described method of the present specification is stored. In some possible embodiments, aspects of the present invention may also be implemented in the form of a program product comprising program code for causing the program product to run on a terminal device when the program product is run The terminal device performs the steps according to various exemplary embodiments of the present invention described in the above-mentioned "Example Method" section of this specification.

Referring to FIG. 6, a program product 600 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read only memory (CD-ROM) and include program codes, and can be used in a terminal device, For example running on a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal in baseband or as part of a carrier wave with readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable signal medium can also be any readable medium, other than a readable storage medium, that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural Programming Language - such as the "C" language or similar programming language. The program code may execute entirely on the user computing device, partly on the user device, as a stand-alone software package, partly on the user computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).

Furthermore, the above-mentioned figures are merely schematic illustrations of the processes included in the methods according to the exemplary embodiments of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the chronological order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, in multiple modules.

Other embodiments of the present disclosure will readily suggest themselves to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or techniques in the technical field not disclosed by the present disclosure . The specification and examples are to be regarded as exemplary only, with the true scope and spirit of the disclosure being indicated by the claims.

It is to be understood that the present disclosure is not limited to the precise structures described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (12)

1. a long-distance personnel intrusion detection method under no illumination condition, is characterized in that, described method comprises: Collect environmental data based on lidar, generate lidar data, and send the lidar data to the main controller; After receiving the lidar data sent by the lidar, the main controller analyzes the lidar data, presets a first threshold based on the size of the intruder, determines and generates a camera control signal, and controls the camera to control the signal to the camera; After the camera receives the camera control signal sent by the main controller, it analyzes and extracts the position information in the camera control signal, operates the pan-tilt rotation of the camera according to the camera control signal, and makes the camera focal length. Focusing on the corresponding position in the position information, collecting the image of the shown position, and generating an image signal; Based on the image signal, an intruder identification process is performed, the characteristics of the intruder are judged, and an alarm signal is generated. 2. The method of claim 1, wherein the method further comprises: Collect environmental data based on lidar, and generate lidar data as electric cloud lidar data. 3. The method of claim 1, wherein the method further comprises: After receiving the laser radar data sent by the laser radar, the main controller analyzes the laser radar data, compares the laser radar data with the preset background data, generates foreign object intrusion data, and analyzes the foreign object. The intrusion data is compared with a preset first threshold of the size of the intruder to determine whether the foreign object intrusion data meets the intrusion standard. 4. The method according to claim 3, wherein the intruder judgment based on lidar in the method further comprises: In the background learning step, when the lidar is installed and initialized, the environment is cleared, the preset frame point cloud is collected, and then the point cloud is stored in the octree structure, and the number of point clouds in each grid and the number of points appearing in each grid are counted. Probability, the grid with a probability greater than 80% is determined as a grid containing background points, otherwise it is determined that there are no points in the grid or the points are noise points; In the background update step, when no foreign object is detected in the environment, the background update is performed every preset time period; In the step of screening the point cloud of the intruder, the point in the real-time point cloud is searched for the point with the closest distance in the background octree structure. If the point corresponding to the point in the real-time point cloud is found within the preset distance threshold range, then Determine that the point in the real-time point cloud is the background, otherwise determine that the point in the real-time point cloud is the point of the intruder; In the target clustering step, when it is determined that a point in the real-time point cloud is a point of an intruder, all points in the real-time point cloud are clustered based on Euclidean distance information, and the size of the clustered object is smaller than the size of the first cluster. If the class threshold is greater than the second clustering threshold, it is determined as a noise point, otherwise it is determined as an intruder, and size and position information of the intruder is detected and generated. 5. The method of claim 1, wherein the method further comprises: The position information of the camera coverage area is divided into a plurality of preset areas, and after receiving the camera control signal sent by the main controller, the camera analyzes and extracts the position information in the camera control signal, The pan/tilt of the camera is operated to rotate according to the camera control signal, and the focal length of the camera is focused to the corresponding preset area in the position information. 6. The method of claim 1, wherein the method further comprises: Based on the YOLOV5 algorithm, the image signal is processed for intruder identification, the characteristics of the intruder are judged, and an alarm signal is generated. 7. The method of claim 1, wherein the method further comprises: After receiving the lidar data sent by the lidar, the main controller analyzes the lidar data, presets a second threshold based on the size of the intruder, and generates an alarm signal. 8. The method of claim 4, wherein the method further comprises: When it is determined that a point in the real-time point cloud is a point of an intruder, all points in the real-time point cloud are clustered based on the Euclidean distance information, and the size of the clustered object is greater than the third clustering threshold, and it is determined as The lidar is blocked by foreign objects and generates an alarm signal. 9. The method of claim 1, 7 or 8, wherein the method further comprises: After the alarm signal is generated, the alarm signal and the image signal corresponding to the alarm signal are sent to the remote terminal based on the communication module. 10. A device for detecting remote personnel intrusion under no-light conditions, wherein the device comprises: Lidar, for collecting environmental data, generating Lidar data, and sending the Lidar data to the main controller; The main controller is used to analyze the lidar data after receiving the lidar data sent by the lidar, and preset a first threshold based on the size of the intruder, judge and generate a camera control signal, and use the The camera control signal is sent to the camera; The camera is used for analyzing and extracting the position information in the camera control signal after receiving the camera control signal sent by the main controller, operating the pan-tilt of the camera to rotate according to the camera control signal, and making all The focal length of the camera is focused to the corresponding position in the position information, the image of the shown position is collected, an image signal is generated, the intruder identification processing is performed based on the image signal, the characteristics of the intruder are judged, and an alarm signal is generated. 11. An electronic device, characterized in that it comprises processor; and a memory having computer readable instructions stored thereon, the computer readable instructions implementing the method according to any one of claims 1 to 9 when executed by the processor. 12. A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 9.

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