CN113489942A - Video monitoring system based on edge calculation - Google Patents
Video monitoring system based on edge calculation Download PDFInfo
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- CN113489942A CN113489942A CN202110587867.1A CN202110587867A CN113489942A CN 113489942 A CN113489942 A CN 113489942A CN 202110587867 A CN202110587867 A CN 202110587867A CN 113489942 A CN113489942 A CN 113489942A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 37
- 238000004364 calculation method Methods 0.000 title claims abstract description 20
- 238000007781 pre-processing Methods 0.000 claims abstract description 25
- 238000012545 processing Methods 0.000 claims abstract description 22
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
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Abstract
A video monitoring system based on edge calculation is characterized by comprising an edge calculation model building module, a calculation center, a video monitoring module, an edge preprocessing module and a cooperative processing module; the computing center is in data connection with the edge computing model building module, the video monitoring module, the edge preprocessing module and the cooperative processing module; the processing and response speed of the monitoring terminal is increased, and the working efficiency is improved.
Description
Technical Field
The invention belongs to the field of edge calculation application, and particularly relates to a video monitoring system based on edge calculation.
Background
The video monitoring system oriented to the public safety field mainly solves the public safety problems of illegal crime, social management and the like, is mainly used for video processing, target query, personnel tracking and the like, and gradually becomes an important guarantee for urban public safety. The traditional video monitoring system has the defects of less built-in computing resources of a front-end camera, larger data volume, higher transmission bandwidth delay, lower target tracking efficiency and the like, and is based on the defects that the video resolution ratio acquired by the front-end camera of the traditional video monitoring system is higher, the video data volume is larger, the video processing capacity of the traditional intelligent monitoring system is insufficient, the computing and transmission bandwidth load of the traditional cloud mode video monitoring system is heavier, and the problems of larger missing detection of target information detection, low detection efficiency and the like are caused.
Disclosure of Invention
The invention provides a video monitoring system based on edge calculation, which accelerates the processing and response speed of a monitoring terminal and improves the working efficiency.
The invention is realized by the following modes:
a video monitoring system based on edge calculation is characterized by comprising an edge calculation model building module, a calculation center, a video monitoring module, an edge preprocessing module and a cooperative processing module; the computing center is in data connection with the edge computing model building module, the video monitoring module, the edge preprocessing module and the cooperative processing module.
Further, the edge calculation model building module comprises a design phase model, a deployment phase model and an operation phase model.
Furthermore, the video monitoring module comprises a plurality of paths of camera data stream hardware structures, each path of camera data stream hardware structure comprises a camera assembly and a raspberry group assembly, and the raspberry group assembly is used as relay equipment and is connected between the camera assembly and the rear-end computing center in a data mode and used for acquiring video streams shot by the camera assembly and capturing images of people and vehicles from the acquired video streams; and the back-end computing center is used for acquiring the image captured by the raspberry pi assembly and analyzing and identifying the acquired image.
Further, the edge preprocessing module comprises a video preprocessing unit and a behavior-aware edge preprocessing unit.
Further, the computing center performs target tracking by: adding a hardware unit for video processing at an acquisition end, copying acquired video information, and then preprocessing (such as target detection), wherein the part of the work uses the existing algorithm to perform the work, after a target position is obtained, a camera transmits useful information to a neighbor node of the camera, and simultaneously receives information sent by the neighbor node, the node performs information fusion on the received information and self-measured information, so as to extract the useful information, and then sends the fused information to the neighbor node, and multiple similar information transmissions are completed in adjacent moments, so that the whole network information is consistent, and the information of each camera can be fused to realize global information sharing in a distributed manner; and finally, forming a robust target tracking system by using a state estimation algorithm.
The invention has the beneficial effects that: the processing and response speed of the monitoring terminal is accelerated, and the working efficiency is improved
Video monitoring has high requirements on computing power and cost, conditions for completing intelligent security at a video monitoring terminal become mature day by day along with image identification and hardware technology development, the problems of untimely cloud computing response and high power consumption are solved, and the requirements of security industry on real-time business, safety, privacy protection and the like are met, so that the video monitoring system is widely applied.
Compared with the traditional video monitoring, the most main change of the edge calculation and the video monitoring is to change passive monitoring into active analysis and early warning, so that the problem that massive monitoring data needs to be manually processed is solved. In essence, the edge computing removes redundant information by preprocessing a video image, so that part or all of video analysis is migrated to the edge, thereby reducing the demands on cloud center computing, storage and network bandwidth and improving the video analysis speed. In addition, the edge preprocessing can also adopt methods such as software optimization, hardware acceleration and the like, so that the video image analysis efficiency is improved.
Taking a face recognition camera as an example, the operation processing capacity of the camera terminal is enhanced, so that the face recognition function of the camera terminal does not depend on a cloud server, the identification is directly completed on local equipment, the time consumption for uploading images is avoided, and the bandwidth resource is saved.
Optimizing a data storage mechanism, saving energy consumption:
the storage link has a direct influence on the intelligent degree of the monitoring system, and particularly under the background of the current deep learning technology development, a video monitoring data elastic storage mechanism based on behavior perception is constructed, so that a monitoring scene behavior perception data processing mechanism becomes more and more important.
The edge calculation provides a platform with a preprocessing function for a video monitoring system so as to extract and analyze the behavior characteristics in the video in real time and adjust the video data according to the behavior characteristic decision function, so that invalid video storage is reduced, the storage space is reduced, the 'in-fact' evidence video data is stored to the maximum extent, the reliability of evidence information is enhanced, and the utilization rate of the video data storage space is improved.
Detailed Description
A video monitoring system based on edge calculation is characterized by comprising an edge calculation model building module, a calculation center, a video monitoring module, an edge preprocessing module and a cooperative processing module; the computing center is in data connection with the edge computing model building module, the video monitoring module, the edge preprocessing module and the cooperative processing module.
Further, the edge calculation model building module comprises a design phase model, a deployment phase model and an operation phase model.
Furthermore, the video monitoring module comprises a plurality of paths of camera data stream hardware structures, each path of camera data stream hardware structure comprises a camera assembly and a raspberry group assembly, and the raspberry group assembly is used as relay equipment and is connected between the camera assembly and the rear-end computing center in a data mode and used for acquiring video streams shot by the camera assembly and capturing images of people and vehicles from the acquired video streams; and the back-end computing center is used for acquiring the image captured by the raspberry pi assembly and analyzing and identifying the acquired image.
Further, the edge preprocessing module comprises a video preprocessing unit and a behavior-aware edge preprocessing unit.
Further, the computing center performs target tracking by: adding a hardware unit for video processing at an acquisition end, copying acquired video information, and then preprocessing (such as target detection), wherein the part of the work uses the existing algorithm to perform the work, after a target position is obtained, a camera transmits useful information to a neighbor node of the camera, and simultaneously receives information sent by the neighbor node, the node performs information fusion on the received information and self-measured information, so as to extract the useful information, and then sends the fused information to the neighbor node, and multiple similar information transmissions are completed in adjacent moments, so that the whole network information is consistent, and the information of each camera can be fused to realize global information sharing in a distributed manner; and finally, forming a robust target tracking system by using a state estimation algorithm.
The system reference architecture is designed based on a model-driven engineering method.
First, a real-time, systematic cognitive model is built for the physical world. Predicting the state of the physical world in the digital world, simulating the operation of the physical world, simplifying the reconstruction of the physical world, and then driving the optimized operation of the physical world. The full life cycle data of the physical world and the business process data can be established and cooperated, and the business process and the production process can be cooperated.
Secondly, interaction is carried out between systems, between subsystems, between services and old systems and the like based on modeled interfaces, and integration is simplified. Based on the model, decoupling of a software interface from development languages, platforms, tools, protocols and the like can be realized, so that cross-platform transplantation is simplified.
The edge calculation model construction module performs model definition from an ICT (information and communication technology) view angle of edge calculation, and comprises the following steps: designing a stage model: and defining the identification, the attribute, the function, the performance, the derivative inheritance relationship and the like of the ECN node, and providing value information for the deployment and operation stages. Deploying a phase model: the method mainly comprises models such as a business strategy and a physical topology. The business strategy model describes business rules and constraints by using a business language instead of a machine language, and realizes business-driven edge computing infrastructure. The business strategy model can be described, can be flexibly multiplexed and changed, and enables the business to be agile. Operating a stage model: the method mainly comprises a connection calculation Fabric model, an operation load model and the like. Based on the models, the system running state can be monitored and optimized, the deployment optimization of the load on the edge distributed architecture is realized, and the like.
The video preprocessing unit is used for removing redundant information of the video images, so that part or all of video analysis is migrated to the edge, the computing, storage and network bandwidth requirements on a cloud center are reduced, and the video image analysis efficiency is improved;
and the behavior-aware edge preprocessing unit is used for realizing the elastic storage of the video data. According to the behavior characteristic decision function, the video data are adjusted in real time, so that the storage of invalid videos is reduced, the storage space is reduced, the 'in-the-fact' evidence video data are stored to the maximum extent, and the utilization rate of the storage space of the video data is improved.
A cooperative processing unit: the system comprises a task publisher, a task receiver and a data processing node, wherein the task publisher is used by police to provide management of tracking tasks, the task receiver is used for receiving the tasks, diffusing the tasks, acquiring video streams and processing partial tasks, and the data processing node is a group of pure computing nodes close to the task receiver.
Claims (5)
1. A video monitoring system based on edge calculation is characterized by comprising an edge calculation model building module, a calculation center, a video monitoring module, an edge preprocessing module and a cooperative processing module; the computing center is in data connection with the edge computing model building module, the video monitoring module, the edge preprocessing module and the cooperative processing module.
2. The system of claim 1, wherein the edge computation model building module comprises a design phase model, a deployment phase model, and a runtime phase model.
3. The system of claim 1, wherein the video surveillance module comprises a plurality of camera data stream hardware structures, each of the camera data stream hardware structures comprising a camera component and a raspberry component, the raspberry component being connected as relay device data between the camera component and the back-end computing center for capturing the video stream captured by the camera component and capturing images of people and vehicles from the captured video stream; and the back-end computing center is used for acquiring the image captured by the raspberry pi assembly and analyzing and identifying the acquired image.
4. The system of claim 1, wherein the edge pre-processing module comprises a video pre-processing unit and a behavior-aware edge pre-processing unit.
5. The system of claim 1, wherein the computing center performs target tracking by: adding a hardware unit for video processing at an acquisition end, copying acquired video information, and then preprocessing (such as target detection), wherein the part of the work uses the existing algorithm to perform the work, after a target position is obtained, a camera transmits useful information to a neighbor node of the camera, and simultaneously receives information sent by the neighbor node, the node performs information fusion on the received information and self-measured information, so as to extract the useful information, and then sends the fused information to the neighbor node, and multiple similar information transmissions are completed in adjacent moments, so that the whole network information is consistent, and the information of each camera can be fused to realize global information sharing in a distributed manner; and finally, forming a robust target tracking system by using a state estimation algorithm.
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| CN202110587867.1A CN113489942A (en) | 2021-05-28 | 2021-05-28 | Video monitoring system based on edge calculation |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114491648A (en) * | 2022-04-02 | 2022-05-13 | 北京嘉沐安科技有限公司 | Block chain data privacy protection method for video live broadcast social big data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114491648A (en) * | 2022-04-02 | 2022-05-13 | 北京嘉沐安科技有限公司 | Block chain data privacy protection method for video live broadcast social big data |
| CN114491648B (en) * | 2022-04-02 | 2022-10-25 | 上海饼戈信息科技有限公司 | Block chain data privacy protection method for video live broadcast social big data |
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