CN111782869B - Video big data event library construction method and device and computer equipment - Google Patents
Video big data event library construction method and device and computer equipment Download PDFInfo
- Publication number
- CN111782869B CN111782869B CN202010654163.7A CN202010654163A CN111782869B CN 111782869 B CN111782869 B CN 111782869B CN 202010654163 A CN202010654163 A CN 202010654163A CN 111782869 B CN111782869 B CN 111782869B
- Authority
- CN
- China
- Prior art keywords
- video
- event
- recording
- stream data
- video stream
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/71—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Signal Processing (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Library & Information Science (AREA)
- Software Systems (AREA)
- Television Signal Processing For Recording (AREA)
Abstract
The invention discloses a method and a device for constructing a video big data event library and computer equipment, wherein the method comprises the following steps: acquiring video stream data and a video recording starting instruction; recording video stream data according to a video recording starting instruction, and recording the recording starting time and camera information corresponding to the video stream data; acquiring a video recording ending instruction; stopping recording the video stream data according to the video recording ending instruction to generate an event video; acquiring event type information for calibrating an event video and determining a storage instruction; and sending the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined storage instruction to form a video big data event library. By implementing the method, the video big data event library is successfully constructed, useful data can be provided for subsequent automatic identification and tracking of the target sample, and the construction method of the video big data event library is simple.
Description
Technical Field
The invention relates to the technical field of video big data, in particular to a method and a device for constructing a video big data event library and computer equipment.
Background
With the rapid development of automation technology, the detection and identification technology of moving objects is receiving more and more attention. The detection and identification technology of the moving target is one of important branches of machine vision, and is used for extracting the moving target from a complicated background, identifying and understanding the moving target, providing a basis for next-step target tracking and the like, and being a key step of image analysis. In an intelligent video monitoring system, the key link is identification and recognition of a moving target, once a suspected moving target appears, the intelligent video monitoring system can automatically give an alarm, and a plurality of defects existing in manual monitoring are greatly improved: such as time and labor waste, and missed inspection caused by long-time visual inspection fatigue.
However, with the development of the times, the data volume acquired by the camera of the video monitoring system is more and more huge, and in order to extract the most useful information from the data to realize automatic identification and tracking of the moving target, the data acquired by the camera of the video monitoring system needs to be mined firstly. Therefore, it is necessary to build a video big data event library for moving objects.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for building a video big data event library, and a computer device, so as to build the video big data event library for a moving object.
According to a first aspect, an embodiment of the present invention provides a method for constructing a video big data event library, including: acquiring video stream data and a video recording starting instruction; recording video stream data according to a video recording starting instruction, and recording the recording starting time and camera information corresponding to the video stream data; acquiring a video recording ending instruction; stopping recording the video stream data according to the video recording ending instruction to generate an event video; acquiring event type information for calibrating an event video and determining a storage instruction; and sending the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined storage instruction to form a video big data event library.
Optionally, the video big data event library construction method further includes: recording the end time of recording and the address of video stream data; and generating an ID number and warehousing time according to the determined warehousing instruction, and sending the ID number, the warehousing time, the event type information, the recording start time, the recording end time, the video stream address, the camera information corresponding to the video stream data and the storage address information of the event video to a preset second storage address to form a metadata base.
Optionally, the method for generating a video big data event library includes the steps of sending an event video to a preset first storage address according to event type information, a recording start time, camera information corresponding to video stream data and a storage-determining instruction, and forming the video big data event library, including: forming name information of the event video according to the recorded starting time and camera information corresponding to the video stream data; and sending the event video to a preset first storage address according to the event type information, the determined warehousing instruction and the name information of the event video to form a video big data event library.
Optionally, the first storage address is a Hadoop cluster address of the remote server, and before the video stream data and the video recording start instruction are acquired, the method for constructing the video big data event library further includes: acquiring connection information for connecting a remote server Hadoop cluster; and connecting the Hadoop cluster of the remote server according to the connection information of the Hadoop cluster of the remote server.
Optionally, the second storage address is a remote server MySQL database address, and before acquiring the video stream data and the video recording start instruction, the method for constructing the video big data event library further includes: acquiring connection information for connecting a MySQL database of a remote server; and connecting the remote server MySQL database according to the connection information of the remote server MySQL database.
According to a second aspect, an embodiment of the present invention provides a video big data event library construction apparatus, including: the first acquisition module is used for acquiring video stream data and a video recording starting instruction; the recording starting module is used for recording video stream data according to the video recording starting instruction and recording the recording starting time and the camera information corresponding to the video stream data; the second acquisition module is used for acquiring a video recording ending instruction; the recording stopping module is used for stopping recording the video stream data according to the video recording ending instruction to generate an event video; the third acquisition module is used for acquiring event type information for calibrating the event video and determining a warehousing instruction; and the first sending module is used for sending the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined storage instruction to form a video big data event library.
Optionally, the video big data event library constructing device further includes; the recording module is used for recording the end time of recording and the address of the video stream data; and the second sending module is used for generating an ID number and warehousing time according to the determined warehousing instruction, and sending the ID number, the warehousing time, the event type information, the recording starting time, the recording ending time, the video stream address, the camera information corresponding to the video stream data and the storage address information of the event video to a preset second storage address to form a metadata base.
Optionally, the first sending module comprises: the forming submodule is used for forming name information of the event video according to the recorded starting time and the camera information corresponding to the video stream data; and the sending submodule is used for sending the event video to a preset first storage address according to the event type information, the determined warehousing instruction and the name information of the event video to form a video big data event library.
According to a third aspect, an embodiment of the present invention provides a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the processor, the instructions being executable by the at least one processor to cause the at least one processor to perform the video big data event library construction method as in the first aspect or any of the embodiments of the first aspect.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause a computer to execute the video big data event library construction method as in the first aspect or any implementation manner of the first aspect.
The method, the device and the computer equipment for constructing the video big data event library provided by the embodiment of the invention record video stream data according to a video recording starting instruction by acquiring the video stream data and the video recording starting instruction, record the recording starting time and the camera information corresponding to the video stream data, acquire a video recording ending instruction, stop recording the video stream data according to the video recording ending instruction to generate an event video, acquire event type information for calibrating the event video and determine a warehousing instruction, send the event video to a preset first storage address according to the event type information, the recording starting time, the camera information corresponding to the video stream data and the determined warehousing instruction, thereby forming the video big data event library, and being capable of providing useful data for the subsequent automatic identification and tracking of target samples, and the video big data event library construction method is simple.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart illustrating a video big data event library construction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a main interface of video big data event library creation software according to an embodiment of the present invention;
FIG. 3 illustrates a software interface diagram for video recording according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a Normal event storage structure according to an embodiment of the present invention;
FIG. 5 illustrates a software interface diagram for a remote server connection according to an embodiment of the present invention;
fig. 6 is a block diagram showing the structure of a video big data event library construction device according to an embodiment of the present invention;
fig. 7 shows a block diagram of a computer apparatus of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for constructing a video big data event library, which comprises the following steps of:
s101, acquiring video stream data and a video recording starting instruction.
Specifically, video big data event library building software can be adopted to record and store videos. The main interface of the software is shown in fig. 2 and comprises 5 parts of a menu bar, a tool bar, a view bar, a log bar and a list bar. The menu bar module mainly realizes the functions of database connection, database management, event calibration, copyright statement and the like. The toolbar mainly realizes convenient operations of database connection, event calibration, video stream data playing and the like. The view bar is mainly used for the display of video stream data. The log column is mainly used for displaying log feedback when the software is operated and displaying database connection success information. The list bar is used for displaying the added video stream data address, and double clicking can select the address to play the video stream.
The format of the video stream data can be RTSP, a certain camera address is selected by double clicking in a list bar, a RTSP stream picture pushed by the current camera in real time can be displayed in a view bar by clicking playing, the video stream data is played, and when an event needing to be calibrated (such as a ship event) occurs, the video can be recorded by clicking a calibration start button.
And S102, recording video stream data according to the video recording starting instruction, and recording the recording starting time and the camera information corresponding to the video stream data.
Specifically, as shown in fig. 3, after the calibration is started, the software automatically records the start time of recording and the camera information corresponding to the video stream data. The camera information is the ID number of the camera.
S103, acquiring a video recording ending instruction; specifically, the video recording end instruction may be issued by clicking an end calibration button.
S104, stopping recording the video stream data according to the video recording ending instruction to generate an event video; specifically, clicking the end calibration button will end the time video recording and buffer the recorded video.
S105, acquiring event type information for calibrating an event video and determining a storage instruction; specifically, when video recording is finished, software automatically pops up a dialog box to prompt the user to select an event type, the user can pop up the dialog box again to select whether to determine warehousing after the event type is selected and the user clicks a determination button to send a warehousing instruction.
And S106, sending the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined storage instruction to form a video big data event library.
Specifically, the first storage address may be a local storage address, or may be a storage address of a remote server. The first memory address may be a Hadoop (Hadoop Distributed File System, HDFS) cluster address. In the embodiment of the invention, HDFS can be adopted to realize distributed storage of video data. HDFS divides each video into a plurality of blocks for storage, the size of each Block is defaulted to 64M, and the size of each Block is insufficient for 64M, storage is carried out according to the actual size, and space waste is avoided. And after sending the warehousing instruction, the software automatically sends the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined warehousing instruction to form a video big data event library. For example, the event type is Normal, and a Normal event storage structure is shown in fig. 4.
The method for constructing the video big data event library provided by the embodiment of the invention obtains the video stream data and the video recording starting instruction, recording video stream data according to the video recording start instruction, recording the recording start time and the camera information corresponding to the video stream data, acquiring a video recording end instruction, stopping recording the video stream data according to the video recording ending instruction, generating an event video, acquiring event type information for calibrating the event video and determining a warehousing instruction, sending the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined warehousing instruction, therefore, a video big data event library is formed, useful data can be provided for subsequent automatic identification and tracking of the target sample, and the video big data event library construction method is simple.
In an optional embodiment, the video big data event library construction method further includes: recording the end time of recording and the address of video stream data; and generating an ID number and warehousing time according to the determined warehousing instruction, and sending the ID number, the warehousing time, the event type information, the recording start time, the recording end time, the video stream address, the camera information corresponding to the video stream data and the storage address information of the event video to a preset second storage address to form a metadata base.
Specifically, the second storage address may be a local storage address, or may be a storage address of a remote server. The metadata database may be a MySQL database and the second storage address may be a MySQL database address. The storage address information of the event video includes a first storage address of the event video. After the click is finished and the calibration is finished, the software automatically records the finishing time and the address information of the video stream. And after clicking a storage determining button, automatically generating an ID number and storage time, uploading the recorded event video to a first storage address, storing the ID number, the storage time, the event type information, the recording start time, the recording end time, the video stream address, the camera information corresponding to the video stream data and the storage address information of the event video in MySQL, and establishing a metadata base. The table structure of the metadata base is shown in table 1.
TABLE 1
By establishing the relational metadata base, the storage information in the metadata base can be checked, the storage information in the metadata base is inquired conditionally to obtain the information of the event video stored in the Hadoop, the metadata can be deleted (the corresponding video data in the Hadoop can be deleted at the same time), the video data stored in the Hadoop can be downloaded according to the address of the event video recorded in the metadata base, and the management of a video big data event base is realized.
In an optional embodiment, the method for generating a video big data event library includes the steps of sending an event video to a preset first storage address according to event type information, a recording start time, camera information corresponding to video stream data and a storage-determining instruction, and forming the video big data event library, including: forming name information of the event video according to the recorded starting time and camera information corresponding to the video stream data; and sending the event video to a preset first storage address according to the event type information, the determined warehousing instruction and the name information of the event video to form a video big data event library.
Specifically, after a storage instruction is sent, the software automatically names the event video according to the recorded starting time and the camera information corresponding to the video stream data, and stores the event video in an event type folder of a first storage address to form a video big data event library.
The name information of the event video is formed according to the recorded starting time and the camera information corresponding to the video stream data, so that the event video can be searched in a big data event library according to the recorded starting time, the camera information corresponding to the video stream data and the like, and the first storage address and the second storage address can be associated according to the recorded starting time and the camera information corresponding to the video stream data.
In an optional embodiment, if the first storage address is a Hadoop cluster address of the remote server, before acquiring the video stream data and the video recording start instruction, a connection with the remote server needs to be established first, and then the video big data event library construction method further includes: acquiring connection information for connecting a remote server Hadoop cluster; and connecting the Hadoop cluster of the remote server according to the connection information of the Hadoop cluster of the remote server.
Specifically, as shown in fig. 5, the connection information of the Hadoop cluster of the remote server includes a server address, a database name, a user password, and a port, and the connection information can be connected to the Hadoop cluster by inputting the server address, the database name, the user password, and the port and clicking the connection.
The event video is stored in the Hadoop cluster by setting the first storage address as the Hadoop cluster address of the remote server, so that the centralized storage and management of the event video acquired by a plurality of clients can be realized, and the management of a video big database is facilitated.
In an optional embodiment, the second storage address is a remote server MySQL database address, and before acquiring the video stream data and the video recording start instruction, the method for constructing the video big data event library further includes: acquiring connection information for connecting a MySQL database of a remote server; and connecting the remote server MySQL database according to the connection information of the remote server MySQL database.
Specifically, the connection information of the remote server MySQL database comprises a server address, a database name, a user password and a port, and the remote server MySQL database can be connected to the MySQL database by inputting the server address, the database name, the user password and the port and clicking the connection.
The second storage address is set as the MySQL database address of the remote server, and the metadata of the event video is stored in the MySQL database, so that the centralized storage and management of the metadata of the event video acquired by a plurality of clients can be realized, and the management of the metadata of the video big database is facilitated.
An embodiment of the present invention further provides a device for constructing a video big data event library, as shown in fig. 6, including:
a first obtaining module 61, configured to obtain video stream data and a video recording start instruction; the specific implementation manner is described in detail in step S101 of the above embodiment, and is not described again here.
A recording start module 62, configured to record video stream data according to a video recording start instruction, and record a recording start time and camera information corresponding to the video stream data; the specific implementation manner is described in detail in step S102 of the above embodiment, and is not described again here.
A second obtaining module 63, configured to obtain a video recording end instruction; the specific implementation manner is described in detail in step S103 of the above embodiment, and is not described again here.
A recording stopping module 64, configured to stop recording the video stream data according to the video recording ending instruction, and generate an event video; the specific implementation manner is described in detail in step S104 of the above embodiment, and is not described herein again.
A third obtaining module 65, configured to obtain event type information for calibrating an event video and determine a storage instruction; the specific implementation manner is described in detail in step S105 of the above embodiment, and is not described again here.
And the first sending module 66 is configured to send the event video to a preset first storage address according to the event type information, the recording start time, the camera information corresponding to the video stream data, and the determined storage instruction, so as to form a video big data event library. The specific implementation manner is described in detail in step S106 of the above embodiment, and is not described herein again.
The video big data event library construction device provided by the embodiment of the invention obtains the video stream data and the video recording starting instruction, recording video stream data according to the video recording start instruction, recording the recording start time and the camera information corresponding to the video stream data, acquiring a video recording end instruction, stopping recording the video stream data according to the video recording ending instruction, generating an event video, acquiring event type information for calibrating the event video and determining a warehousing instruction, sending the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined warehousing instruction, therefore, a video big data event library is formed, useful data can be provided for subsequent automatic identification and tracking of the target sample, and the video big data event library construction method is simple.
In an optional embodiment, the video big data event library construction device further comprises; the recording module is used for recording the end time of recording and the address of the video stream data; and the second sending module is used for generating an ID number and warehousing time according to the determined warehousing instruction, and sending the ID number, the warehousing time, the event type information, the recording starting time, the recording ending time, the video stream address, the camera information corresponding to the video stream data and the storage address information of the event video to a preset second storage address to form a metadata base.
In an alternative embodiment, the first sending module 66 includes: the forming submodule is used for forming name information of the event video according to the recorded starting time and the camera information corresponding to the video stream data; and the sending submodule is used for sending the event video to a preset first storage address according to the event type information, the determined warehousing instruction and the name information of the event video to form a video big data event library.
An embodiment of the present invention further provides a computer device, as shown in fig. 7, including: a processor 31 and a memory 32, wherein the processor 31 and the memory 32 may be connected by a bus or other means, and fig. 7 illustrates the connection by the bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32, which is a non-transitory computer readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the video big data event library construction method in the embodiment of the present invention. The processor 31 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 32, namely, the video big data event library construction method in the above method embodiment is realized.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more of the modules described above are stored in the memory 32 and, when executed by the processor 31, perform the video big data event library construction method in the embodiment shown in fig. 1.
The details of the computer device can be understood with reference to the corresponding related descriptions and effects in the embodiment shown in fig. 1, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (6)
1. A video big data event library construction method is characterized by comprising the following steps:
acquiring video stream data and a video recording starting instruction;
recording the video stream data according to the video recording starting instruction, and recording the recording starting moment and the camera information corresponding to the video stream data;
acquiring a video recording ending instruction;
stopping recording the video stream data according to the video recording ending instruction to generate an event video;
acquiring event type information for calibrating the event video and determining a storage instruction;
sending the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined storage instruction to form a video big data event library;
recording the end time of recording and the address of the video stream data;
generating an ID number and warehousing time according to the determined warehousing instruction, sending the ID number, the warehousing time, the event type information, the recording start time, the recording end time, the video stream address, the camera information corresponding to the video stream data and the storage address information of the event video to a preset second storage address to form a metadata base, and associating the first storage address with the second storage address according to the recording start time and the camera information corresponding to the video stream data;
the method for forming the video big data event library comprises the following steps of sending an event video to a preset first storage address according to the event type information, the recorded starting time, the camera information corresponding to the video stream data and a determined storage instruction, and forming the video big data event library, wherein the method comprises the following steps:
forming name information of the event video according to the event type information, the recording starting time and camera information corresponding to the video stream data;
and sending the event video to a preset first storage address according to the event type information, the determined storage instruction and the name information of the event video to form a video big data event library.
2. The method for constructing the video big data event library according to claim 1, wherein the first storage address is a Hadoop cluster address of a remote server, and before the acquiring the video stream data and the video recording start instruction, the method further comprises:
acquiring connection information for connecting the Hadoop cluster of the remote server;
and connecting the remote server Hadoop cluster according to the connection information of the remote server Hadoop cluster.
3. The method for constructing a video big data event library according to claim 1, wherein the second storage address is a remote server MySQL database address, and further comprising, before the acquiring the video stream data and the video recording start command:
acquiring connection information for connecting the remote server MySQL database;
and connecting the remote server MySQL database according to the connection information of the remote server MySQL database.
4. A video big data event library construction device is characterized by comprising:
the first acquisition module is used for acquiring video stream data and a video recording starting instruction;
the recording starting module is used for recording the video stream data according to the video recording starting instruction and recording the recording starting time and the camera information corresponding to the video stream data;
the second acquisition module is used for acquiring a video recording ending instruction;
the recording stopping module is used for stopping recording the video stream data according to the video recording ending instruction to generate an event video;
the third acquisition module is used for acquiring event type information for calibrating the event video and determining a warehousing instruction;
the first sending module is used for sending the event video to a preset first storage address according to the event type information, the recording starting moment, the camera information corresponding to the video stream data and the determined storage instruction to form a video big data event library;
the recording module is used for recording the end time of recording and the address of the video stream data;
the second sending module is used for generating an ID number and warehousing time according to the determined warehousing instruction, sending the ID number, the warehousing time, the event type information, the recording starting time, the recording ending time, the address of the video stream, the camera information corresponding to the video stream data and the storage address information of the event video to a preset second storage address to form a metadata base, and associating the first storage address with the second storage address according to the recording starting time and the camera information corresponding to the video stream data;
wherein the first transmitting module comprises:
the forming submodule is used for forming name information of the event video according to the recorded starting time and camera information corresponding to video stream data;
and the sending submodule is used for sending the event video to a preset first storage address according to the event type information, the determined warehousing instruction and the name information of the event video to form a video big data event library.
5. A computer device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the video big data event library construction method of any one of claims 1-3.
6. A computer-readable storage medium storing computer instructions for causing a computer to execute the video big data event library construction method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010654163.7A CN111782869B (en) | 2020-07-08 | 2020-07-08 | Video big data event library construction method and device and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010654163.7A CN111782869B (en) | 2020-07-08 | 2020-07-08 | Video big data event library construction method and device and computer equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111782869A CN111782869A (en) | 2020-10-16 |
| CN111782869B true CN111782869B (en) | 2022-02-18 |
Family
ID=72758492
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010654163.7A Active CN111782869B (en) | 2020-07-08 | 2020-07-08 | Video big data event library construction method and device and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111782869B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113784071A (en) * | 2021-09-07 | 2021-12-10 | 上海万物新生环保科技集团有限公司 | Video processing method and device for evidence obtaining |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060104601A1 (en) * | 2004-11-15 | 2006-05-18 | Ati Technologies, Inc. | Method and apparatus for programming the storage of video information |
| CN102082947B (en) * | 2009-11-26 | 2013-02-20 | 中国移动通信集团公司 | Method, system and apparatus for video monitoring |
| CN107580260A (en) * | 2016-07-04 | 2018-01-12 | 北京新岸线网络技术有限公司 | A kind of verifying video content method and system |
| US20180113462A1 (en) * | 2016-10-22 | 2018-04-26 | Gopro, Inc. | Position-based soft stop for a 3-axis gimbal |
| CN109166373A (en) * | 2018-09-12 | 2019-01-08 | 深圳点猫科技有限公司 | It is a kind of for educating the content of courses store method and system of operating system |
| CN109271452B (en) * | 2018-10-19 | 2021-04-13 | 武汉达梦数据库有限公司 | DB2 database data synchronous updating method and device |
-
2020
- 2020-07-08 CN CN202010654163.7A patent/CN111782869B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111782869A (en) | 2020-10-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101810578B1 (en) | Automatic media sharing via shutter click | |
| CN111522922A (en) | Log information query method and device, storage medium and computer equipment | |
| KR101841180B1 (en) | Place-based image organization | |
| CN107209773B (en) | Automatic invocation of unified visual interface | |
| CN106537380A (en) | Automated archiving of user generated media files | |
| WO2016142638A1 (en) | Anonymous live image search | |
| CN110750694A (en) | Data annotation implementation method and device, electronic equipment and storage medium | |
| US20200092520A1 (en) | Computer implemented systems frameworks and methods configured for enabling review of incident data | |
| CN110908920A (en) | Interface function testing method and device and related components | |
| CN110879780A (en) | Page abnormity detection method and device, electronic equipment and readable storage medium | |
| CN111782869B (en) | Video big data event library construction method and device and computer equipment | |
| KR102040525B1 (en) | Artificial intelligence based part search system | |
| CN112040312A (en) | Split-screen rendering method, device, equipment and storage medium | |
| CN111104542A (en) | Part identification management method and device | |
| CN113824987A (en) | Method, medium, device and computing equipment for determining time consumption of first frame of live broadcast room | |
| CN106682210B (en) | Log file query method and device | |
| CN111522749A (en) | Page testing method and device, readable storage medium and electronic equipment | |
| CN111294613A (en) | Video processing method, client and server | |
| CN107291870B (en) | Method for reading files in distributed storage in batch | |
| CN111522570B (en) | Target library updating method and device, electronic equipment and machine-readable storage medium | |
| CN111966605A (en) | Automatic resource retrieval method, system and storage medium for Redfish | |
| US11416269B2 (en) | Method, system and computer program product for serving user settings interface components | |
| CN110909798A (en) | Multi-algorithm intelligent studying and judging method, system and server | |
| CN111898640B (en) | Method and device for pushing pictures by analog snapshot machine, test system and electronic equipment | |
| CN111013156B (en) | Scene detection method, device, terminal and medium based on robot |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |