WO2020057346A1 - Video monitoring method and apparatus, monitoring server and video monitoring system - Google Patents

Abstract

The present invention relates to the technical field of video monitoring and especially relates to a video monitoring method and apparatus, a monitoring server and a video monitoring system. The method comprises: when an abnormal picture image is detected by means of video data photographed by a target camera, predicting a future movement trajectory of a target object in the abnormal picture image; determining whether the future movement trajectory of the target object is separated from monitored area coverage of a monitoring server; and according to the determination result, monitoring the target object. The method can both automatically detect whether an abnormal picture image exists in a video to implement video monitoring so that comprehensive and effective monitoring can be performed in advance on a target object in the abnormal picture image; and further implement monitoring by predicting a future movement trajectory of the target object so that comprehensive and effective monitoring can be further guaranteed.

Description

Video monitoring method and device, monitoring server and video monitoring system

This application claims the priority of a Chinese patent application filed on September 21, 2018 with the Chinese Patent Office under the application number CN201811120075.8 and the invention name "Video Monitoring Method and Device, Monitoring Server and Video Monitoring System" Incorporated by reference in this application.

Technical field

The present invention relates to the technical field of video surveillance, and in particular, to a video surveillance method and device, a surveillance server, and a video surveillance system.

Background technique

Video surveillance is an efficient surveillance technology with real-time, reliability, and intuitive features. It is easy to use and has attracted attention from all walks of life. However, traditional video surveillance systems must have dedicated personnel to monitor the video in real time, and they need to take the initiative to determine whether the video is abnormal. It cannot provide early warning of possible dangers and can only watch the videos after the dangers occur. Moreover, traditional methods cannot predict risk factors in advance for effective monitoring.

Summary of the Invention

An object of the embodiments of the present invention is to provide a video monitoring method and device, a monitoring server and a video monitoring system, which can automatically detect abnormal conditions to implement effective monitoring.

To solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

In a first aspect, an embodiment of the present invention provides a video surveillance method, where the method includes:

When an abnormal picture image is detected through the video data captured by the target camera, predicting the future movement trajectory of the target object in the abnormal picture image;

Determining whether the future movement trajectory of the target object deviates from the monitoring area of the monitoring server;

According to the judgment result, the target object is monitored.

Optionally, the monitoring the target object according to a judgment result includes:

If the future movement trajectory of the target object deviates from the monitoring area range of the monitoring server, determine other cameras whose shooting range covers the future movement trajectory of the target object;

Determining whether the other cameras are connected to the same surveillance server as the target camera;

If yes, call the video data shot by the other cameras;

If not, calling the video data of the other monitoring server connected to the other camera, wherein the video data of the other monitoring server is transmitted by the other camera to the other monitoring server;

If the future movement trajectory of the target object does not depart from the monitoring area range of the monitoring server, continue monitoring the target object.

Optionally, the invoking video data captured by the other cameras includes:

Determining a target geographic location involved in a future movement trajectory of the target object;

Send a video call request to other monitoring servers that manage other cameras located at the target geographic location, so that the other monitoring server returns video data captured by the other cameras according to the video call request.

Optionally, the detecting an abnormal picture image by the video data captured by the target camera includes:

Obtain video detection anomaly models;

Determining whether a picture image in video data captured by the target camera matches the video detection abnormal model;

If they match, the screen image is used as the abnormal screen image;

If there is no match, the screen image is taken as a normal screen image.

Optionally, the method further includes:

Obtaining a training video data set, where the training video data set includes video data of multiple abnormal scenes;

Preprocessing the video data of the multiple abnormal scenes;

The preprocessed video data is processed by a convolution algorithm to establish the video detection abnormal model.

Optionally, the predicting the future movement trajectory of the target object in the abnormal picture image includes:

Determining the geographic location of the target object in the abnormal picture image;

Obtaining a topographic map of the geographic location where the target object is located;

Predicting a future movement trajectory of the target object according to the current moving direction of the target object and the topographic map.

In a second aspect, an embodiment of the present invention provides a video surveillance device, where the device includes:

A prediction module, configured to predict a future movement trajectory of a target object in the abnormal picture image when an abnormal picture image is detected through the video data captured by the target camera;

A judging module, configured to judge whether a future movement trajectory of the target object deviates from a monitoring area range of the monitoring server;

A monitoring module is configured to monitor the target object according to a judgment result.

Optionally, the monitoring module is specifically configured to:

If the future movement trajectory of the target object deviates from the monitoring area range of the monitoring server, determine other cameras whose shooting range covers the future movement trajectory of the target object;

Determining whether the other cameras are connected to the same surveillance server as the target camera;

If yes, call the video data shot by the other cameras;

If not, calling the video data of other surveillance servers connected to the other cameras, wherein the video data of the other surveillance servers is transmitted by the other cameras to the other surveillance servers;

If the future movement trajectory of the target object does not depart from the monitoring area range of the monitoring server, continue monitoring the target object.

In a third aspect, an embodiment of the present invention provides a monitoring server, including:

At least one processor; and

A memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processing The device can be used to perform the video surveillance method according to any one of the above.

In a fourth aspect, an embodiment of the present invention provides a video monitoring system, including:

Several cameras

In the monitoring server, the monitoring server communicates with the camera.

In a fifth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause a monitoring server to execute The video surveillance method according to any one.

In a sixth aspect, an embodiment of the present invention provides a computer program product. The computer program product includes a computer program stored on a non-volatile computer-readable storage medium. The computer program includes program instructions. When the instruction is executed by the monitoring server, the monitoring server is caused to execute the video monitoring method according to any one of the foregoing.

In the video monitoring method and device, monitoring server, and video monitoring system provided by various embodiments of the present invention, first, when an abnormal picture image is detected through video data captured by a target camera, a future movement trajectory of a target object in the abnormal picture image is predicted; Secondly, determine whether the future movement trajectory of the target object deviates from the monitoring area of the monitoring server; again, monitor the target object based on the judgment result. On the one hand, it can automatically detect whether there is an abnormal picture image in the video, and implement video surveillance in this way, so that it can fully and effectively monitor the target object in the abnormal picture image in advance. On the other hand, it can also perform monitoring by predicting the future movement trajectory of the target object, thereby further ensuring comprehensive and effective monitoring.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments are exemplified by the pictures in the accompanying drawings. These exemplary descriptions do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the drawings in the drawings do not constitute a limitation on scale.

FIG. 1 is a schematic structural diagram of a video surveillance system according to an embodiment of the present invention;

2 is a schematic flowchart of a video monitoring method according to an embodiment of the present invention;

3 is a schematic structural diagram of a video monitoring device according to an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of a monitoring server according to an embodiment of the present invention.

detailed description

In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

The video monitoring method in the embodiment of the present invention can be executed in any suitable type of client with computing capability, such as a monitoring server, a desktop computer, a smart phone, a tablet computer, and other electronic products. The monitoring server here may be a physical server or a logical server virtualized by multiple physical servers. The server may also be a server group composed of multiple servers that can communicate with each other, and each functional module may be separately distributed on each server in the server group.

The video surveillance device according to the embodiment of the present invention may be used as a software system and independently set in the above-mentioned client, or may be one of the functional modules integrated in the processor to execute the video surveillance method of the embodiment of the present invention.

Please refer to FIG. 1, which is a schematic structural diagram of a video surveillance system according to an embodiment of the present invention. As shown in FIG. 1, the video surveillance system 100 includes a plurality of cameras 11, a surveillance server 12, and a mobile terminal 13.

The camera 11 is installed in a preset area for collecting video data. It can be understood that the camera 11 is fixedly installed in a preset area according to a preset rule, so as to cover the preset area as much as possible. For example, the high-definition camera is arranged on a wall surface, a ground, a roof, or an object surface of the preset area in combination with the specific structure and occlusion of the preset area.

Each camera forms a camera group, which is used to monitor a specific monitoring area range, and each camera is installed at a preset position in the specific monitoring area range. Generally, each camera in the camera group uploads the collected video data to the same monitoring server. Different monitoring areas correspond to different monitoring servers. For different managers who manage different surveillance areas, the surveillance servers of the two do not share surveillance video with each other.

In order to increase the shooting angle and shooting range of the camera 11, reduce the deployment of the camera 11, and reduce the system cost, a combination of the camera 11 and a multi-dimensional rotating motor can be used to capture real-time capture of high-definition video frame images in the preset area. Of course, you can choose an integrated camera 11 instead of the combination of the multi-dimensional rotating motor and the camera 11, such as a hemispherical all-in-one machine, a fast dome all-in-one machine, an integrated high-definition camera combined with a gimbal, or an integrated machine with a built-in lens. All-in-one can achieve automatic focusing. Preferably, a high-definition camera with a waterproof function, a small size, a high resolution, a long life, and a universal communication interface is selected.

In some embodiments, the camera 11 is a network camera, and the camera 11 has a built-in network coding module.

The camera includes a lens, an image sensor, a sound sensor, an A / D converter, a controller, a control interface, a network interface, and so on. The camera may be used to collect video data signals, and the video data signals are analog video signals. The camera is mainly composed of a CMOS light-sensitive component and a peripheral circuit, and is used for converting an optical signal input from the lens into an electrical signal.

Specifically, the network coding module has an embedded chip built therein, the embedded chip is used to convert the video data signals collected by the camera into digital signals, the video data signals are analog video signals, and the embedded chip also The digital signal may be compressed. Specifically, the embedded chip may be a Hi3516 high-efficiency compression chip.

The camera 11 sends the compressed digital signal to the monitoring server 12 through the WIFI network. The monitoring server 12 may send the compressed digital signal to the mobile terminal 13. The camera 11 further includes an infrared sensor, so that the camera 11 has a night vision function. Users on the network can directly view the camera image on the web server with a browser or directly access through the mobile terminal APP. The camera 11 can more easily implement monitoring, especially remote monitoring, with simple construction and maintenance, better support for audio, Better support for alarm linkage, more flexible recording storage, richer product selection, higher-definition video effects and more perfect monitoring and management functions, and the camera can be directly connected to the local area network, which is the data collection and photoelectric signal The conversion end is the data supply end of the entire network.

The monitoring server 12 is a device that provides computing services. The composition of the monitoring server includes a processor, a hard disk, a memory, a system bus, and the like. Similar to a general computer architecture, the monitoring server is responsible for providing functions such as mobile terminal APP registration, user management, and device management. At the same time, it is responsible for the video data storage function of the camera, and remembers the IP and port of the mobile terminal and camera through the monitoring server, and transmits the IP and port of the corresponding mobile terminal and camera to each other, so that the camera and mobile end can know The other party's IP and port establish a connection and communication through the IP address and port. The monitoring server obtains the video data of the camera and then analyzes the video data according to the artificial intelligence module. When abnormal video data is detected, it sends an alarm message to notify the mobile terminal.

Specifically, the monitoring server 12 includes a processor, and the processor includes an artificial intelligence module. The artificial intelligence module is responsible for real-time analysis of video data, detects abnormal times, and notifies the mobile terminal. The specific implementation of the artificial intelligence module is divided into two parts, the establishment of a video anomaly detection model and the application of a video anomaly detection model. The first is the establishment of the video anomaly detection model. There are three parts. The first part: training the video data set of the video anomaly detection model for the training and learning of the subsequent machines. It includes video data of various abnormal scenes, such as frequent crossing of vehicles, robbery, trailing theft, fights, group fights, screams, crying, smoke, noisy video data, and other abnormal scenes that need to be detected. The training video dataset covers most application scenarios. The second part: the preprocessing of the video data set. The video data is extracted 10 pictures per second, and each picture is converted into a picture of 255 pixels long and 255 pixels wide. The third part: the establishment of training model, using artificial intelligence convolution algorithm, Python code to build the training model. The model includes an input layer, a hidden layer, and an output layer. The input layer is an input pre-processed picture. The hidden layer is used to calculate the features of the input picture. The output layer is based on the calculated features of the hidden layer to output whether the video contains abnormal scenes. The training process is. The normal video is marked as 0 and the abnormal video is marked as 1. Then, the abnormal video and the normal video are input into the training system at the same time, and the data set is preprocessed and the training model is calculated to distinguish whether the video is abnormal or normal. Repeat the above steps, stop training when the accuracy of the system's discrimination reaches 90%, and save the model. After the model is established, the model is transferred to the server, the data set is replaced with the video of the camera, and the model is run to detect whether the video of the camera is abnormal.

Please refer to FIG. 2, which is a schematic flowchart of a video surveillance method according to an embodiment of the present invention. As shown in FIG. 2, the video monitoring method S200 includes:

S21. When an abnormal picture image is detected through the video data captured by the target camera, predict the future movement trajectory of the target object in the abnormal picture image;

In this embodiment, when the monitoring server detects an abnormal picture image through the video data captured by the target camera, it first obtains a video detection abnormal model. Secondly, the monitoring server judges whether the picture image in the video data captured by the target camera matches the video detection abnormal model; again, if it matches, the monitoring server uses the picture image as the abnormal picture image; if it does not match, the monitoring server uses the picture image as the normal picture image.

In this embodiment, the video detection abnormality model may be constructed in advance. For example, first, the monitoring server obtains a training video data set, and the training video data set includes video data of various abnormal scenes. Secondly, the monitoring server preprocesses the video data of various abnormal scenes. Third, the monitoring server processes the pre-processed video data through a convolution algorithm to establish a video detection anomaly model.

When the monitoring server detects the abnormal picture image, the monitoring server analyzes the abnormal picture image according to the image analysis algorithm, and extracts the target object from the abnormal picture image, for example, extracts a vehicle that violates the rules or is interspersed or interspersed or turned around. Secondly, the monitoring server predicts the future movement trajectory of the target object in the abnormal picture image. The future movement trajectory is the direction that the target object may move in the subsequent time relative to the current monitoring time. For example, first, the monitoring server determines the geographic location of the target object in the abnormal picture image. Secondly, the monitoring server obtains a topographic map of the geographical location of the target object. Third, the monitoring server predicts the future movement trajectory of the target object based on the current moving direction and topographic map of the target object. For example, the target vehicle makes a U-turn on a one-way street and travels in the opposite direction until it may drive out of the original monitoring area and change to another monitoring server. Managed monitoring area.

S22. Determine whether the future movement trajectory of the target object deviates from the monitoring area of the monitoring server;

S23. Monitor the target object according to the judgment result.

In this embodiment, if the future movement trajectory of the target object deviates from the monitoring area range of the monitoring server, the monitoring server determines other cameras whose shooting range covers the future movement trajectory of the target object. Second, the monitoring server determines whether the other cameras are connected to the target camera. Monitor the server; if so, call the video data captured by other cameras. If not, call the video data of other surveillance servers connected to other cameras, where the video data of other surveillance servers are transmitted by other cameras to other surveillance servers. Therefore, the user can continuously and comprehensively monitor the target object without interruption.

If the future movement trajectory of the target object does not depart from the monitoring area range of the monitoring server, continue monitoring the target object.

In summary, on the one hand, it can automatically detect whether there is an abnormal picture image in the video, and implement video surveillance in this way, so that it can make a comprehensive and effective monitoring of the target object in the abnormal picture image in advance. On the other hand, it can also perform monitoring by predicting the future movement trajectory of the target object, thereby further ensuring comprehensive and effective monitoring.

In some embodiments, when the monitoring server calls video data captured by other cameras, the monitoring server determines the target geographic location involved in the future movement trajectory of the target object, and sends a video call request to other monitoring servers that manage other cameras located at the target geographic location, In order to make other monitoring servers return video data captured by other cameras according to the video call request. Therefore, the monitoring server can view video data captured by other cameras managed by other monitoring servers.

In some embodiments, the monitoring server controls the PTZ of the target camera to adjust the camera lens to follow the movement of the target object according to the movement of the target object. In some embodiments, when the target camera tracks the target object, the monitoring server may draw and save the walking path of the target object, so as to provide convenience when the target object is subsequently analyzed.

Since the target object is the focus of the monitoring server, in order to analyze the target object with the help of high-definition images later, the monitoring server can enlarge the video image containing the target object in order to obtain a more detailed picture of the target object. Or, in order to fully restore the surrounding environment of the target object at a later stage, the monitoring server may also reduce the video image containing the target object to obtain a larger field of view including the target object as much as possible.

Generally, when there are some abnormal situations in a video scene, the objects in the video scene are more worthy of attention, such as anomalies such as robbery, trailing theft, fights, group fights, screams, crying, smoke or noisy. The objects in the scene are worth paying attention to. Therefore, in some embodiments, when the monitoring server controls the target camera to track the target object, first, the monitoring server determines whether the target video frame containing the target object matches the preset video detection abnormal model; if it matches, the target video frame is used as the tracking starting point. To control the target camera to track the target object. If they do not match, continue to determine whether the next target video frame containing the target object matches the preset video detection abnormal model.

In some embodiments, the target object is a person, the number of cameras is at least two, and different cameras can shoot people from different angles. The monitoring server uses the target video frame as the tracking start point. When the target camera is controlled to track the target object, the monitoring server first uses the target video frame as the tracking start point to obtain the person's image taken by the target camera.

Secondly, the monitoring server determines whether the person image is a front image of the person, and the front image includes a face image of the person. For example, A's Trailer B, Opportunity Pickpocket B's handbag, the camera monitors A's Trailing action behavior, and sends video data containing A's Trailing action behavior to the monitoring server. The monitoring server detects A's Trailing action behavior and determines A is the target person. The monitoring server then analyzes the person's image according to the image analysis algorithm to determine whether there are facial feature points associated with the target person in the video data. If it exists, it considers that the video data contains a frontal image of the target person; if it does not exist, it considers the video data The front image of the target person is not included, and the video data includes only the back image of the target person. For example, following the above example, if the monitoring server detects the face image of A in the video data, it is considered that the target camera has captured the front image of A. If the surveillance server does not detect the face image of nail A in the video data, the target camera is considered to have captured the back image of nail A.

Again, if it is a frontal image of a person, the monitoring server controls the target camera to track the person; if not, the monitoring server detects an additional camera set opposite to the target camera, controls the additional camera to take a frontal image of the person, and tracks the person. For example, when the monitoring server detects that the video data does not include a frontal image of the target person, the monitoring server determines the current geographic location of the target person.

Secondly, the monitoring server detects and covers all the additional cameras of the target person's current geographical position and determines the installation geographical positions of all the additional cameras according to the current geographical position of the target person, and determines the relationship with the target camera from the installation geographical positions of all the additional cameras. Install additional cameras that are relatively geographically located.

Again, the surveillance server controls an additional camera relative to the installed geographic location of the target camera to track the person and take a frontal image of the person.

In fact, most of the vicious events occur in dark places such as low light. In order to prevent illegal elements and strive to obtain high-definition face images of illegal elements, in some embodiments, the monitoring server detects additional cameras that are set opposite to the target camera. First, the monitoring server obtains the light intensity in the preset area. For example, a light sensor set in the preset area collects the light intensity and transmits the light intensity to the monitoring server.

Secondly, the monitoring server judges whether the light intensity is greater than a preset intensity threshold. If it is greater than that, it obtains the minimum illumination values of all the additional cameras set relative to the target camera, and traverses the additional cameras with the lowest minimum illumination value from the lowest illumination values of all the additional cameras. As a camera that tracks and captures the front image of a person, the surveillance server obtains the front image of the person in high definition as much as possible. If it is less than that, an additional camera set relative to the target camera is detected.

In this way, it can obtain high-definition frontal images of people as much as possible, thereby achieving effective video surveillance.

It should be noted that, in the above embodiments, there is not necessarily a certain sequence between the above steps. Those skilled in the art can understand from the description of the embodiments of the present invention that the above steps in different embodiments There can be different execution orders, that is, execution can be performed in parallel, execution can be exchanged, and so on.

As another aspect of the embodiments of the present invention, an embodiment of the present invention provides a video surveillance device. The video monitoring device according to the embodiment of the present invention may be used as one of the software functional units. The video monitoring device includes several instructions stored in a memory, and the processor may access the memory and call the instructions for execution to complete the above-mentioned video monitoring method.

Referring to FIG. 3, the video monitoring device 300 includes a prediction module 31, a determination module 32, and a monitoring module 33.

The prediction module 31 is configured to predict a future movement trajectory of a target object in the abnormal picture image when an abnormal picture image is detected through video data captured by the target camera;

The judging module 32 is configured to judge whether a future movement trajectory of the target object deviates from a monitoring area range of the monitoring server;

The monitoring module 33 is configured to monitor the target object according to a determination result.

In summary, on the one hand, it can automatically detect whether there is an abnormal picture image in the video, and implement video surveillance in this way, so that it can make a comprehensive and effective monitoring of the target object in the abnormal picture image in advance. On the other hand, it can also perform monitoring by predicting the future movement trajectory of the target object, thereby further ensuring comprehensive and effective monitoring.

In some embodiments, the monitoring module 33 is specifically configured to: if the future movement trajectory of the target object deviates from the monitoring area range of the monitoring server, determine other cameras whose shooting range covers the future movement trajectory of the target object; Determine whether the other cameras are connected to the same surveillance server as the target camera; if yes, call the video data captured by the other cameras; if not, call the video data of other surveillance servers connected to the other cameras, where the Video data of other monitoring servers are transmitted by the other cameras to the other monitoring servers; if the future movement trajectory of the target object does not depart from the monitoring area of the monitoring server, continue monitoring the target object.

It should be noted that the above security data uploading device can execute the security data uploading method provided by the embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in the embodiment of the security data uploading device, refer to the security data uploading method provided in the embodiment of the present invention.

As yet another aspect of the embodiments of the present invention, an embodiment of the present invention provides a monitoring server. As shown in FIG. 4, the monitoring server 400 includes: one or more processors 41 and a memory 42. Among them, one processor 41 is taken as an example in FIG. 4.

The processor 41 and the memory 42 may be connected through a bus or other manners. In FIG. 4, the connection through the bus is taken as an example.

The memory 42 is a non-volatile computer-readable storage medium and can be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as program instructions corresponding to the video monitoring method in the embodiment of the present invention. / Module. The processor 41 executes various functional applications and data processing of the video monitoring device by running the non-volatile software programs, instructions, and modules stored in the memory 42, that is, the video monitoring method of the foregoing method embodiment and the device embodiment are implemented The function of each module.

The memory 42 may include a high-speed random access memory, and may further include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 42 may optionally include a memory remotely disposed with respect to the processor 41, and these remote memories may be connected to the processor 41 through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The program instructions / modules are stored in the memory 42 and when executed by the one or more processors 41, perform the video monitoring method in any of the above method embodiments, for example, perform each step of FIG. 2 described above ; Can also achieve the functions of each module described in Figure 3.

An embodiment of the present invention also provides a non-volatile computer storage medium. The computer storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, such as a process in FIG. 4. The processor 41 may cause the one or more processors to execute the video monitoring method in any of the foregoing method embodiments, for example, to execute the video monitoring method in any of the foregoing method embodiments, for example, to execute the foregoing description to perform the foregoing description. The steps shown in FIG. 2 described above; the functions of the modules described in FIG. 3 may also be implemented.

The embodiments of the device or device described above are only schematic, and the unit modules described as separate components may or may not be physically separated, and the components displayed as module units may or may not be physical units. , Can be located in one place, or can be distributed to multiple network module units. Some or all of the modules may be selected according to actual needs to achieve the objective of the solution of this embodiment.

Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus a general hardware platform, and of course, also by hardware. Based on such an understanding, the above-mentioned technical solutions that are essentially or contribute to related technologies can be embodied in the form of software products, which can be stored in computer-readable storage media, such as ROM / RAM, magnetic disks , Optical discs, etc., including several instructions until a computer device (which may be a personal computer, a server, or a network device, etc.) executes the methods described in various embodiments or certain parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to describe the technical solution of the present invention, but not limited thereto. Under the idea of the present invention, the technical features in the above embodiments or different embodiments can also be combined. The steps can be implemented in any order and there are many other variations of the different aspects of the invention as described above, for the sake of brevity they are not provided in the details; although the invention has been described in detail with reference to the foregoing embodiments, it is common in the art The skilled person should understand that they can still modify the technical solutions described in the foregoing embodiments, or equivalently replace some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions separate from the implementation of this application. Examples of technical solutions.

Claims (10)

1. A video monitoring method, characterized in that the method includes:

   When an abnormal picture image is detected through the video data captured by the target camera, predicting the future movement trajectory of the target object in the abnormal picture image;

   Determining whether the future movement trajectory of the target object deviates from the monitoring area of the monitoring server;

   According to the judgment result, the target object is monitored.
2. The method according to claim 1, wherein the monitoring the target object according to a judgment result comprises:

   If the future movement trajectory of the target object deviates from the monitoring area range of the monitoring server, determine other cameras whose shooting range covers the future movement trajectory of the target object;

   Determining whether the other cameras are connected to the same surveillance server as the target camera;

   If yes, call the video data shot by the other cameras;

   If not, calling the video data of the other monitoring server connected to the other camera, wherein the video data of the other monitoring server is transmitted by the other camera to the other monitoring server;

   If the future movement trajectory of the target object does not depart from the monitoring area range of the monitoring server, continue monitoring the target object.
3. The method according to claim 2, wherein the invoking video data captured by the other camera comprises:

   Determining a target geographic location involved in a future movement trajectory of the target object;

   Send a video call request to other monitoring servers that manage other cameras located at the target geographic location, so that the other monitoring server returns video data captured by the other cameras according to the video call request.
4. The method according to any one of claims 1 to 3, wherein the detecting an abnormal picture image by the video data captured by the target camera comprises:

   Obtain video detection anomaly models;

   Judging whether the picture image in the video data captured by the target camera matches the video detection abnormal model;

   If they match, the screen image is used as the abnormal screen image;

   If there is no match, the screen image is taken as a normal screen image.
5. The method according to claim 4, further comprising:

   Obtaining a training video data set, where the training video data set includes video data of multiple abnormal scenes;

   Preprocessing the video data of the multiple abnormal scenes;

   The preprocessed video data is processed by a convolution algorithm to establish the video detection abnormal model.
6. The method according to any one of claims 1 to 3, wherein the predicting a future movement trajectory of a target object in the abnormal picture image comprises:

   Determining the geographic location of the target object in the abnormal picture image;

   Obtaining a topographic map of the geographic location where the target object is located;

   Predicting a future movement trajectory of the target object according to the current moving direction of the target object and the topographic map.
7. A video monitoring device, characterized in that the device includes:

   A prediction module, configured to predict a future movement trajectory of a target object in the abnormal picture image when an abnormal picture image is detected through the video data captured by the target camera;

   A judging module, configured to judge whether a future movement trajectory of the target object deviates from a monitoring area range of the monitoring server;

   A monitoring module is configured to monitor the target object according to a judgment result.
8. The device according to claim 7, wherein the monitoring module is specifically configured to:

   If the future movement trajectory of the target object deviates from the monitoring area range of the monitoring server, determine other cameras whose shooting range covers the future movement trajectory of the target object;

   Determining whether the other cameras are connected to the same surveillance server as the target camera;

   If yes, call the video data shot by the other cameras;

   If not, calling video data of other monitoring servers connected to the other cameras, wherein the video data of the other monitoring servers is transmitted by the other cameras to the other monitoring servers;

   If the future movement trajectory of the target object does not depart from the monitoring area range of the monitoring server, continue monitoring the target object.
9. A monitoring server, comprising:

   At least one processor; and

   A memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processing The device can be used to execute the video surveillance method according to any one of claims 1 to 6.
10. A video surveillance system, comprising:

    Several cameras

    The monitoring server according to claim 9, said monitoring server communicating with said camera.

Images