WO2020057346A1 - 视频监控方法及装置、监控服务器及视频监控*** - Google Patents

视频监控方法及装置、监控服务器及视频监控*** Download PDF

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WO2020057346A1
WO2020057346A1 PCT/CN2019/103766 CN2019103766W WO2020057346A1 WO 2020057346 A1 WO2020057346 A1 WO 2020057346A1 CN 2019103766 W CN2019103766 W CN 2019103766W WO 2020057346 A1 WO2020057346 A1 WO 2020057346A1
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monitoring
target object
video
video data
camera
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PCT/CN2019/103766
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English (en)
French (fr)
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汪巍巍
张恩勇
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深圳市九洲电器有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources

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  • 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.
  • 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.
  • 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.
  • the embodiments of the present invention provide the following technical solutions:
  • an embodiment of the present invention provides a video surveillance method, where the method includes:
  • the target object is monitored.
  • the monitoring the target object according to a judgment result includes:
  • the invoking video data captured by the other cameras includes:
  • the detecting an abnormal picture image by the video data captured by the target camera includes:
  • the screen image is used as the abnormal screen image
  • the screen image is taken as a normal screen image.
  • the method further includes:
  • the training video data set includes video data of multiple abnormal scenes
  • the preprocessed video data is processed by a convolution algorithm to establish the video detection abnormal model.
  • the predicting the future movement trajectory of the target object in the abnormal picture image includes:
  • 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.
  • the monitoring module is specifically configured to:
  • an embodiment of the present invention provides a monitoring server, including:
  • At least one processor At least one processor
  • 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.
  • an embodiment of the present invention provides a video monitoring system, including:
  • the monitoring server communicates with the camera.
  • 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.
  • 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.
  • monitoring server 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.
  • FIG. 1 is a schematic structural diagram of a video surveillance system according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of a video monitoring method according to an embodiment of the present invention.
  • FIG. 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.
  • 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 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.
  • FIG. 1 is a schematic structural diagram of a video surveillance system according to an embodiment of the present invention.
  • 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.
  • 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.
  • 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.
  • 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.
  • a high-definition camera with a waterproof function, a small size, a high resolution, a long life, and a universal communication interface is selected.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • FIG. 2 is a schematic flowchart of a video surveillance method according to an embodiment of the present invention.
  • the video monitoring method S200 includes:
  • the monitoring server 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.
  • 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.
  • the monitoring server 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.
  • 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.
  • the one hand 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.
  • it can also perform monitoring by predicting the future movement trajectory of the target object, thereby further ensuring comprehensive and effective monitoring.
  • the monitoring server 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.
  • 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.
  • 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.
  • 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.
  • the target object is a person
  • the number of cameras is at least two
  • different cameras can shoot people from different angles.
  • the monitoring server uses the target video frame as the tracking start point.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • an embodiment of the present invention provides a video surveillance device.
  • the video monitoring device 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.
  • 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.
  • the one hand 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.
  • it can also perform monitoring by predicting the future movement trajectory of the target object, thereby further ensuring comprehensive and effective monitoring.
  • 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.
  • 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.
  • the security data uploading method provided in the embodiment of the present invention.
  • an embodiment of the present invention provides a monitoring server.
  • 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.
  • 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.
  • 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.

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Abstract

本发明涉及视频监控技术领域，特别是涉及一种视频监控方法及装置、监控服务器及视频监控***。方法包括：通过目标摄像机拍摄的视频数据检测到异常画面图像时，预测异常画面图像中目标物体的未来移动轨迹；判断目标物体的未来移动轨迹是否脱离监控服务器的监控区域范围；根据判断结果，监控目标物体。一方面，其能够自动检测视频中是否存在异常画面图像，以此实施视频监控，从而能够提前作好对异常画面图像中目标物体作出全面有效地监控。另一方面，其还可以通过预测目标物体的未来移动轨迹实施监控，从而进一步保证作出全面有效地监控。

Description

视频监控方法及装置、监控服务器及视频监控***
本申请要求于2018年09月21日提交中国专利局，申请号为CN201811120075.8，发明名称为“视频监控方法及装置、监控服务器及视频监控***”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及视频监控技术领域，特别是涉及一种视频监控方法及装置、监控服务器及视频监控***。
背景技术
视频监控是一种高效的监控技术，具有实时性，可靠性，直观性等特点，使用方便，因而受到了各行各业的关注。然而传统的视频监控***必须有专门的人员去实时监控视频、需要人员主动去判断视频是否有异常，不能对可能发生的危险提前预警，只能在危险发生后回看视频。并且，传统方式也不能***危险因素以作出有效地监控。
发明内容
本发明实施例一个目的旨在提供一种视频监控方法及装置、监控服务器及视频监控***，其能够自动检测出异常情况以实施有效地监控。
为解决上述技术问题，本发明实施例提供以下技术方案：
在第一方面，本发明实施例提供一种视频监控方法，所述方法包括：
通过目标摄像机拍摄的视频数据检测到异常画面图像时，预测所述异常画面图像中目标物体的未来移动轨迹；
判断所述目标物体的未来移动轨迹是否脱离所述监控服务器的监控区域范围；
根据判断结果，监控所述目标物体。
可选地，所述根据判断结果，监控所述目标物体，包括：
若所述目标物体的未来移动轨迹脱离所述监控服务器的监控区域范围，确定拍摄范围覆盖所述目标物体的未来移动轨迹的其它摄像机；
判断所述其它摄像机是否与所述目标摄像机连接同一监控服务器；
若是，调用所述其它摄像机拍摄的视频数据；
若否，调用与所述其它摄像机连接的其它监控服务器的视频数据，其中，所述其它监控服务器的视频数据由所述其它摄像机向所述其它监控服务器传输的；
若所述目标物体的未来移动轨迹未脱离所述监控服务器的监控区域范围，继续监控所述目标物体。
可选地，所述调用所述其它摄像机拍摄的视频数据，包括：
确定所述目标物体的未来移动轨迹涉及的目标地理位置；
向管理位于所述目标地理位置的其它摄像机的其它监控服务器发送视频调用请求，以使所述其它监控服务器根据所述视频调用请求返回所述其它摄像机拍摄的视频数据。
可选地，所述通过目标摄像机拍摄的视频数据检测到异常画面图像，包括：
获取视频检测异常模型；
判断所述目标摄像机拍摄的视频数据中画面图像是否匹配所述视频检测异常模型；
若匹配，将所述画面图像作为异常画面图像；
若未匹配，将所述画面图像作为正常画面图像。
可选地，所述方法还包括：
获取训练视频数据集，所述训练视频数据集包括多种异常场景的视频数据；
对所述多种异常场景的视频数据进行预处理；
通过卷积算法处理预处理后的视频数据，建立所述视频检测异常模型。
可选地，所述预测所述异常画面图像中目标物体的未来移动轨迹，包括：
确定所述异常画面图像中目标物体所处的地理位置；
获取所述目标物体所处的地理位置的地形图；
根据所述目标物体的当前移动方向与所述地形图，预测所述目标物体的未来移动轨迹。
在第二方面，本发明实施例提供一种视频监控装置，所述装置包括：
预测模块，用于通过目标摄像机拍摄的视频数据检测到异常画面图像时，预测所述异常画面图像中目标物体的未来移动轨迹；
判断模块，用于判断所述目标物体的未来移动轨迹是否脱离所述监控服务器的监控区域范围；
监控模块，用于根据判断结果，监控所述目标物体。
可选地，所述监控模块具体用于：
若所述目标物体的未来移动轨迹脱离所述监控服务器的监控区域范围，确定拍摄范围覆盖所述目标物体的未来移动轨迹的其它摄像机；
判断所述其它摄像机是否与所述目标摄像机连接同一监控服务器；
若是，调用所述其它摄像机拍摄的视频数据；
若否，调用与所述其它摄像机连接的其它监控服务器的视频数据，其中，所述其它监控服务器的视频数据由所述其它摄像机向所述其它监控服务器传输的；
若所述目标物体的未来移动轨迹未脱离所述监控服务器的监控区域范围，继续监控所述目标物体。
在第三方面，本发明实施例提供一种监控服务器，包括：
至少一个处理器；以及
与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够用于执行任一项所述的视频监控方法。
在第四方面，本发明实施例提供一种视频监控***，包括：
若干摄像机；
所述的监控服务器，所述监控服务器与所述摄像机通讯。
在第五方面，本发明实施例提供一种非暂态计算机可读存储介质，所述非暂态计算机可读存储介质存储有计算机可执行指令，所述计算机可执行指令用于使监控服务器执行任一项所述的视频监控方法。
在第六方面，本发明实施例提供一种计算机程序产品，所述计算机程序产品包括存储在非易失性计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被监控服务器执行时，使所述监控服务器执行任一项所述的视频监控方法。
在本发明各个实施例提供的视频监控方法及装置、监控服务器及视频监控***中，首先，通过目标摄像机拍摄的视频数据检测到异常画面图像时，预测异常画面图像中目标物体的未来移动轨迹；其次，判断目标物体的未来移动轨迹是否脱离监控服务器的监控区域范围；再次，根据判断结果，监控目标物体。一方面，其能够自动检测视频中是否存在异常画面图像，以此实施视频监控，从而能够提前作好对异常画面图像中目标物体作出全面有效地监控。另一方面，其还可以通过预测目标物体的未来移动轨迹实施监控，从而进一步保证作出全面有效地监控。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。
图1是本发明实施例提供一种视频监控***的结构示意图；
图2是本发明实施例提供一种视频监控方法的流程示意图；
图3是本发明实施例提供一种视频监控装置的结构示意图；
图4是本发明实施例提供一种监控服务器的结构示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的 具体实施例仅用以解释本发明，并不用于限定本发明。
本发明实施例的视频监控方法，可以在任何合适类型、具有运算能力的客户端中执行，例如监控服务器、台式计算机、智能手机、平板电脑以及其他电子产品中。其中，此处的监控服务器可以是一个物理服务器或者多个物理服务器虚拟而成的一个逻辑服务器。服务器也可以是多个可互联通信的服务器组成的服务器群，且各个功能模块可分别分布在服务器群中的各个服务器上。
本发明实施例的视频监控装置可以作为软件***，独立设置在上述客户端中，也可以作为整合在处理器中的其中一个功能模块，执行本发明实施例的视频监控方法。
请参阅图1，图1是本发明实施例提供一种视频监控***的结构示意图。如图1所示，视频监控***100包括若干摄像机11、监控服务器12及移动终端13。
摄像机11安装于预设区域内，用于采集视频数据。可以理解的是，摄像机11按照预设规律固定安装于预设区域，尽可能地做到将所述预设区域全部覆盖。例如，在所述预设区域的墙面、地面、屋顶或者物体表面，结合所述预设区域的具体结构和遮挡等布设所述高清摄像机。
各个摄像机组成一个摄像机群，用于监控特定监控区域范围，每个摄像机安装于该特定监控区域范围中预设位置。一般的，摄像机群中各个摄像机皆将采集的视频数据上传至同一监控服务器。不同监控区域范围，对应着不同监控服务器。对于管理不同监控区域的不同管理者，两者的监控服务器互不共享监控视频。
为提高摄像机11的拍摄角度和拍摄范围，减少摄像机11的布设，降低***成本，可以采用摄像机11与多维旋转电机结合的方式对预设区域进行高清视频帧图像的实时捕抓。当然，可以选择一体化的摄像机11替代多维旋转电机与摄像机11结合的方式，比如，半球形一体机、快速球型一体机、结合云台的一体化高清摄像机或镜头内置的一体机等，上述的一体机可以实现自动聚焦。优选的，选择具有防水功能、体积较小、分辨率高、高寿命以及具有通用通信接口等的高清摄像机。
在一些实施例中，摄像机11为网络摄像机，摄像机11内置有网络编码模块。
摄像机包括镜头、图像传感器、声音传感器、A/D转换器、控制器、控制接口、网络接口以及等等。所述摄像机可以用于采集视频数据信号，所述视频数据信号为模拟视频信号。所述摄像机主要由CMOS光敏元器件和***电路组成，用于将所述镜头传入的光信号转换为电信号。
具体的，网络编码模块内置一嵌入式芯片，所述嵌入式芯片用于将所述摄像机采集到的视频数据信号转换为数字信号，所述视频数据信号为模拟视频信号，所述嵌入式芯片还可以将所述数字信号进行压缩。具体的，所述嵌入式芯片可以为Hi3516高效压缩芯片。
摄像机11通过WIFI网络将压缩后的数字信号发送到监控服务器12。监控服务器12可以将压缩后的数字信号发送到移动终端13。其中，摄像机11还包括红外传感器，使得摄像机11具有夜视功能。网络上用户可以直接用浏览器观看Web服务器上的摄像机图像或者通过移动终端APP直接访问，摄像机11能更简单地实现监控，特别是远程监控，具有简单的施工和维护、更好的支持音频、更好的支持报警联动、更灵活的录像存储、更丰富的产品选择、更高清的视频效果和更完美的监控管理功能，并且可直接将摄像机接入本地局域网，是数据的采集和光电信号的转换端，是整个网络的数据提供端。
其中，监控服务器12是提供计算服务的设备。监控服务器的构成包括处理器、硬盘、内存、***总线等，和通用的计算机架构类似，监控服务器负责提供移动终端APP的注册登录，用户的管理，设备管理等功能。同时负责摄像机的视频数据的存储功能，以及通过监控服务器记住移动终端和摄像机的IP和端口，将对应的移动终端和摄像机的IP和端口都传送给对方，从而使摄像机端和移动端能知道对方的IP和端口，通过IP地址和端口建立二者的连接通信。监控服务器获取摄像机的视频数据然后根据人工智能模块去分析视频数据，当检测到异常的视频数据时就会发送告警信息通知所述移动终端。
具体的，监控服务器12包括一处理器，所述处理器包括人工智能 模块。所述人工智能模块负责对视频数据的实时分析，检测异常的时刻并通知移动终端。人工智能模块的具体实施方式分为，视频异常检测模型的建立和视频异常检测模型的应用两个部分。首先是视频异常检测模型的建立分这三个部分，第一部分：训练视频异常检测模型的视频数据集，用于后面的机器的训练和学习。包括各种异常场景的视频数据如行驶车辆频繁穿插并线、抢劫、尾随盗窃、打架斗殴、群殴、尖叫声，哭泣声、烟雾，嘈杂的视频数据等多种需要检测的异常场景。训练视频数据集覆盖大部分的应用场景。第二部分：视频数据集的预处理，将视频数据按一秒钟抽取10张图片，每张图片转换为长255像素和宽255像素的图片。第三部分：训练模型的建立，使用人工智能的卷积算法，Python代码建立训练的模型。模型包括输入层，隐藏层，输出层，输入层是输入预处理的图片，隐藏层用来计算输入图片的特征，输出层是通过隐藏层的计算特征输出该视频是否包含异常场景。训练的过程是。将正常的视频标记为0异常的视频标记为1，然后将异常的视频和正常的视频同时输入训练***，通过数据集预处理和训练模型的计算，分辨视频是异常视频还是正常的视频。重复上面的步骤，当***分辨的正确率达到90％以上停止训练，保存模型。建立完模型后，将模型转移到服务器端，将数据集换成摄像机的视频，运行模型，检测摄像机的视频是否有异常的情况。
请参阅图2，图2是本发明实施例提供一种视频监控方法的流程示意图。如图2所示，视频监控方法S200包括：
S21、通过目标摄像机拍摄的视频数据检测到异常画面图像时，预测异常画面图像中目标物体的未来移动轨迹；
在本实施例中，监控服务器通过目标摄像机拍摄的视频数据检测到异常画面图像时，首先获取视频检测异常模型。其次，监控服务器判断目标摄像机拍摄的视频数据中画面图像是否匹配视频检测异常模型；再次，若匹配，监控服务器将画面图像作为异常画面图像；若未匹配，监控服务器将画面图像作为正常画面图像。
在本实施例中，视频检测异常模型可被预先构建。例如，首先，监 控服务器获取训练视频数据集，训练视频数据集包括多种异常场景的视频数据。其次，监控服务器对多种异常场景的视频数据进行预处理。再次，监控服务器通过卷积算法处理预处理后的视频数据，建立视频检测异常模型。
当监控服务器检测到异常画面图像时，监控服务器根据图像分析算法分析异常画面图像，从异常画面图像中提取出目标物体，例如，提取出违规并线或穿插或掉头的车辆。其次，监控服务器预测异常画面图像中目标物体的未来移动轨迹，未来移动轨迹为相对于当前监控时间，目标物体在后续时间内可能移动的方向。例如，首先，监控服务器确定异常画面图像中目标物体所处的地理位置。其次，监控服务器获取目标物体所处的地理位置的地形图。再次，监控服务器根据目标物体的当前移动方向与地形图，预测目标物体的未来移动轨迹，例如，目标车辆在单行道上掉头逆向行驶，直至可能行驶出原来的监控区域而变换到受另一监控服务器管理的监控区域。
S22、判断目标物体的未来移动轨迹是否脱离监控服务器的监控区域范围；
S23、根据判断结果，监控目标物体。
在本实施例中，若目标物体的未来移动轨迹脱离监控服务器的监控区域范围，监控服务器确定拍摄范围覆盖目标物体的未来移动轨迹的其它摄像机；其次，监控服务器判断其它摄像机是否与目标摄像机连接同一监控服务器；若是，调用其它摄像机拍摄的视频数据。若否，调用与其它摄像机连接的其它监控服务器的视频数据，其中，其它监控服务器的视频数据由其它摄像机向其它监控服务器传输的。因此，用户能够不间断连续地对目标物体实施全面监控。
若目标物体的未来移动轨迹未脱离所述监控服务器的监控区域范围，继续监控目标物体。
综上，一方面，其能够自动检测视频中是否存在异常画面图像，以此实施视频监控，从而能够提前作好对异常画面图像中目标物体作出全面有效地监控。另一方面，其还可以通过预测目标物体的未来移动轨迹 实施监控，从而进一步保证作出全面有效地监控。
在一些实施例中，监控服务器调用其它摄像机拍摄的视频数据时，监控服务器确定目标物体的未来移动轨迹涉及的目标地理位置，向管理位于目标地理位置的其它摄像机的其它监控服务器发送视频调用请求，以使其它监控服务器根据视频调用请求返回其它摄像机拍摄的视频数据。于是，监控服务器便可以查看其它监控服务器管理的其它摄像机拍摄的视频数据。
在一些实施例中，监控服务器根据目标物体的移动，控制目标摄像机的云台调整摄像镜头跟随着目标物体的移动而移动。在一些实施例中，目标摄像机跟踪目标物体时，监控服务器可以绘制并保存目标物体的行走路径，以便后续分析目标物体时，提供便利。
既然目标物体是监控服务器重点关注的对象，为了后期借助高清图像能够分析目标物体，监控服务器可以放大包含目标物体的视频画面，以便获得目标物体的更细节画面。或者，为了后期能够全面还原目标物体的周围环境，监控服务器还可以缩小包含目标物体的视频画面，以尽可能获得包含目标物体的更大视野范围。
一般的，当某个视频场景出现一些异常情况时，该视频场景中的物体更值得重点关注，例如在抢劫、尾随盗窃、打架斗殴、群殴、尖叫声、哭泣声、烟雾或嘈杂等异常场景中的物体是值得重点关注的。因此，在一些实施例中，监控服务器控制目标摄像机跟踪目标物体时，首先，监控服务器判断包含目标物体的目标视频帧是否匹配预设视频检测异常模型；若匹配，以目标视频帧为跟踪起始点，控制目标摄像机跟踪所述目标物体。若未匹配，继续判断包含目标物体的下一帧目标视频帧是否匹配预设视频检测异常模型。
在一些实施例中，目标物体为人物，摄像机的数量为至少两个，不同摄像机可从不同角度拍摄人物。监控服务器以目标视频帧为跟踪起始点，控制目标摄像机跟踪目标物体时，首先监控服务器以目标视频帧为跟踪起始点，获取目标摄像机拍摄人物的人物图像。
其次，监控服务器判断人物图像是否是人物的正面图像，正面图像 包括人物的人脸图像。例如，甲尾随乙，伺机扒手乙的手提包，摄像机监控到甲的尾随动作行为，并将包含甲的尾随动作行为的视频数据发送至监控服务器，监控服务器检测到甲的尾随动作行为，并确定甲为目标人物。监控服务器再根据图像分析算法分析甲的人物图像，判断视频数据是否存在与目标人物关联的人脸特征点，若存在，则认为视频数据包含目标人物的正面图像；若未存在，则认为视频数据未包含目标人物的正面图像，并且该视频数据只包含目标人物的背面图像。例如，承接上述例子，若监控服务器在视频数据检测出甲的人脸图像，则认为目标摄像机拍摄到甲的正面图像。若监控服务器在视频数据未检测出甲的人脸图像，则认为目标摄像机拍摄到甲的背面图像。
再次，若是人物的正面图像，监控服务器控制目标摄像机跟踪人物；若否，监控服务器检测出与目标摄像机相对设置的额外摄像机，控制额外摄像机拍摄人物的正面图像，并跟踪人物。例如，当监控服务器检测出视频数据未包含目标人物的正面图像时，监控服务器确定目标人物的当前地理位置。
其次，监控服务器根据目标人物的当前地理位置，检测与覆盖目标人物的当前地理位置的所有额外摄像机并确定所有额外摄像机的安装地理位置，并从所有额外摄像机的安装地理位置中确定与目标摄像机的安装地理位置相对的额外摄像机。
再次，监控服务器控制与目标摄像机的安装地理位置相对的额外摄像机跟踪人物并拍摄人物的正面图像。
实际上，一些恶性事件发生时间大部分在光线弱等黑暗地方，为了严防非法分子，争取获得非法分子高清人脸图像，在一些实施例中，监控服务器检测出与目标摄像机相对设置的额外摄像机时，首先，监控服务器获取预设区域内的光照强度，例如，设置于预设区域内的光照传感器采集光照强度，并将光照强度传输至监控服务器。
其次，监控服务器判断光照强度是否大于预设强度阈值，若大于，获取与目标摄像机相对设置的所有额外摄像机的最低照度值，从所有额外摄像机的最低照度值中遍历出最低照度值最低的额外摄像机作为跟 踪并拍摄人物的正面图像的摄像机，于是，监控服务器便尽可能地获取到高清的人物正面图像。若小于，检测出与目标摄像机相对设置的额外摄像机。
通过此种方式，其能够尽可能地获取到高清的人物正面图像，从而实现有效地视频监控。
需要说明的是，在上述各个实施例中，上述各步骤之间并不必然存在一定的先后顺序，本领域普通技术人员，根据本发明实施例的描述可以理解，不同实施例中，上述各步骤可以有不同的执行顺序，亦即，可以并行执行，亦可以交换执行等等。
作为本发明实施例的另一方面，本发明实施例提供一种视频监控装置。本发明实施例的视频监控装置可以作为其中一个软件功能单元，视频监控装置包括若干指令，该若干指令存储于存储器内，处理器可以访问该存储器，调用指令进行执行，以完成上述视频监控方法。
请参阅图3，视频监控装置300包括：预测模块31、判断模块32及监控模块33。
预测模块31用于通过目标摄像机拍摄的视频数据检测到异常画面图像时，预测所述异常画面图像中目标物体的未来移动轨迹；
判断模块32用于判断所述目标物体的未来移动轨迹是否脱离所述监控服务器的监控区域范围；
监控模块33用于根据判断结果，监控所述目标物体。
综上，一方面，其能够自动检测视频中是否存在异常画面图像，以此实施视频监控，从而能够提前作好对异常画面图像中目标物体作出全面有效地监控。另一方面，其还可以通过预测目标物体的未来移动轨迹实施监控，从而进一步保证作出全面有效地监控。
在一些实施例中，所述监控模块33具体用于：若所述目标物体的未来移动轨迹脱离所述监控服务器的监控区域范围，确定拍摄范围覆盖所述目标物体的未来移动轨迹的其它摄像机；判断所述其它摄像机是否与所述目标摄像机连接同一监控服务器；若是，调用所述其它摄像机拍摄的视频数据；若否，调用与所述其它摄像机连接的其它监控服务器的 视频数据，其中，所述其它监控服务器的视频数据由所述其它摄像机向所述其它监控服务器传输的；若所述目标物体的未来移动轨迹未脱离所述监控服务器的监控区域范围，继续监控所述目标物体。
需要说明的是，上述安防数据上传装置可执行本发明实施例所提供的安防数据上传方法，具备执行方法相应的功能模块和有益效果。未在安防数据上传装置实施例中详尽描述的技术细节，可参见本发明实施例所提供的安防数据上传方法。
作为本发明实施例的又另一方面，本发明实施例提供一种监控服务器。如图4所示，该监控服务器400包括：一个或多个处理器41以及存储器42。其中，图4中以一个处理器41为例。
处理器41和存储器42可以通过总线或者其他方式连接，图4中以通过总线连接为例。
存储器42作为一种非易失性计算机可读存储介质，可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块，如本发明实施例中的视频监控方法对应的程序指令/模块。处理器41通过运行存储在存储器42中的非易失性软件程序、指令以及模块，从而执行视频监控装置的各种功能应用以及数据处理，即实现上述方法实施例视频监控方法以及上述装置实施例的各个模块的功能。
存储器42可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器42可选包括相对于处理器41远程设置的存储器，这些远程存储器可以通过网络连接至处理器41。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述程序指令/模块存储在所述存储器42中，当被所述一个或者多个处理器41执行时，执行上述任意方法实施例中的视频监控方法，例如，执行以上描述的图2各个步骤；也可实现附图3所述的各个模块的功能。
本发明实施例还提供了一种非易失性计算机存储介质，所述计算机 存储介质存储有计算机可执行指令，该计算机可执行指令被一个或多个处理器执行，例如图4中的一个处理器41，可使得上述一个或多个处理器可执行上述任意方法实施例中的视频监控方法，例如，执行上述任意方法实施例中的视频监控方法，例如，执行以上描述的执行以上描述的执行以上描述的图2所示的各个步骤；也可实现附图3所述的各个模块的功能。
以上所描述的装置或设备实施例仅仅是示意性的，其中所述作为分离部件说明的单元模块可以是或者也可以不是物理上分开的，作为模块单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络模块单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对相关技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用直至得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；在本发明的思路下，以上实施例或者不同实施例中的技术特征之间也可以进行组合，步骤可以以任意顺序实现，并存在如上所述的本发明的不同方面的许多其它变化，为了简明，它们没有在细节中提供；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。

Claims (10)

  1. 一种视频监控方法，其特征在于，所述方法包括：
    通过目标摄像机拍摄的视频数据检测到异常画面图像时，预测所述异常画面图像中目标物体的未来移动轨迹；
    判断所述目标物体的未来移动轨迹是否脱离所述监控服务器的监控区域范围；
    根据判断结果，监控所述目标物体。
  2. 根据权利要求1所述的方法，其特征在于，所述根据判断结果，监控所述目标物体，包括：
    若所述目标物体的未来移动轨迹脱离所述监控服务器的监控区域范围，确定拍摄范围覆盖所述目标物体的未来移动轨迹的其它摄像机；
    判断所述其它摄像机是否与所述目标摄像机连接同一监控服务器；
    若是，调用所述其它摄像机拍摄的视频数据；
    若否，调用与所述其它摄像机连接的其它监控服务器的视频数据，其中，所述其它监控服务器的视频数据由所述其它摄像机向所述其它监控服务器传输的；
    若所述目标物体的未来移动轨迹未脱离所述监控服务器的监控区域范围，继续监控所述目标物体。
  3. 根据权利要求2所述的方法，其特征在于，所述调用所述其它摄像机拍摄的视频数据，包括：
    确定所述目标物体的未来移动轨迹涉及的目标地理位置；
    向管理位于所述目标地理位置的其它摄像机的其它监控服务器发送视频调用请求，以使所述其它监控服务器根据所述视频调用请求返回所述其它摄像机拍摄的视频数据。
  4. 根据权利要求1至3任一项所述的方法，其特征在于，所述通过目标摄像机拍摄的视频数据检测到异常画面图像，包括：
    获取视频检测异常模型；
    判断所述目标摄像机拍摄的视频数据中画面图像是否匹配所述视 频检测异常模型；
    若匹配，将所述画面图像作为异常画面图像；
    若未匹配，将所述画面图像作为正常画面图像。
  5. 根据权利要求4所述的方法，其特征在于，所述方法还包括：
    获取训练视频数据集，所述训练视频数据集包括多种异常场景的视频数据；
    对所述多种异常场景的视频数据进行预处理；
    通过卷积算法处理预处理后的视频数据，建立所述视频检测异常模型。
  6. 根据权利要求1至3任一项所述的方法，其特征在于，所述预测所述异常画面图像中目标物体的未来移动轨迹，包括：
    确定所述异常画面图像中目标物体所处的地理位置；
    获取所述目标物体所处的地理位置的地形图；
    根据所述目标物体的当前移动方向与所述地形图，预测所述目标物体的未来移动轨迹。
  7. 一种视频监控装置，其特征在于，所述装置包括：
    预测模块，用于通过目标摄像机拍摄的视频数据检测到异常画面图像时，预测所述异常画面图像中目标物体的未来移动轨迹；
    判断模块，用于判断所述目标物体的未来移动轨迹是否脱离所述监控服务器的监控区域范围；
    监控模块，用于根据判断结果，监控所述目标物体。
  8. 根据权利要求7所述的装置，其特征在于，所述监控模块具体用于：
    若所述目标物体的未来移动轨迹脱离所述监控服务器的监控区域范围，确定拍摄范围覆盖所述目标物体的未来移动轨迹的其它摄像机；
    判断所述其它摄像机是否与所述目标摄像机连接同一监控服务器；
    若是，调用所述其它摄像机拍摄的视频数据；
    若否，调用与所述其它摄像机连接的其它监控服务器的视频数据，其中，所述其它监控服务器的视频数据由所述其它摄像机向所述其它监 控服务器传输的；
    若所述目标物体的未来移动轨迹未脱离所述监控服务器的监控区域范围，继续监控所述目标物体。
  9. 一种监控服务器，其特征在于，包括：
    至少一个处理器；以及
    与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够用于执行如权利要求1至6任一项所述的视频监控方法。
  10. 一种视频监控***，其特征在于，包括：
    若干摄像机；
    如权利要求9所述的监控服务器，所述监控服务器与所述摄像机通讯。
PCT/CN2019/103766 2018-09-21 2019-08-30 视频监控方法及装置、监控服务器及视频监控*** WO2020057346A1 (zh)

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