CN108040221B - Intelligent video analysis and monitoring system - Google Patents
Intelligent video analysis and monitoring system Download PDFInfo
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- CN108040221B CN108040221B CN201711231668.7A CN201711231668A CN108040221B CN 108040221 B CN108040221 B CN 108040221B CN 201711231668 A CN201711231668 A CN 201711231668A CN 108040221 B CN108040221 B CN 108040221B
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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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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
- H04N19/423—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements
- H04N19/426—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements using memory downsizing methods
- H04N19/427—Display on the fly, e.g. simultaneous writing to and reading from decoding memory
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
- H04N19/436—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation using parallelised computational arrangements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/643—Communication protocols
- H04N21/6437—Real-time Transport Protocol [RTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/14—Picture signal circuitry for video frequency region
- H04N5/144—Movement detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/222—Studio circuitry; Studio devices; Studio equipment
- H04N5/262—Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
- H04N5/272—Means for inserting a foreground image in a background image, i.e. inlay, outlay
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- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Computing Systems (AREA)
- Theoretical Computer Science (AREA)
- Alarm Systems (AREA)
- Closed-Circuit Television Systems (AREA)
Abstract
An intelligent video analysis and monitoring system, wherein front-end equipment is connected with a video storage server, the video storage server is connected with a video analysis server, and the video analysis server is connected with a client; the front-end equipment comprises a network camera and a switch, the video storage server comprises a video stream acquisition module and a video storage module, and the video analysis server comprises a video data receiving module, a video decoding module, a video analysis module, a video display module, an alarm rule setting module, a database operation module and an alarm information pushing module; the client comprises a historical alarm information query module and an alarm response module; the video analysis module analyzes the image by utilizing a CUDA hard decoding technology and a CUDA-based image parallel processing method, so that the occupancy rate of CPU resources is effectively reduced, the analysis process is accelerated by the parallel processing technology, the time consumption of analysis is effectively reduced, and a guarantee is provided for realizing real-time analysis.
Description
Technical Field
The invention relates to the technical field of video monitoring, in particular to an intelligent video analysis and monitoring system.
Background
With the development of pattern recognition technology, intelligent analysis technologies such as detection, recognition and tracking aiming at foreground targets in videos are widely applied to intelligent video monitoring systems. From an analog video monitoring system to a digital video monitoring system, and to a network video monitoring system which is popularized so far, the system is continuously applied to the fields of urban traffic management, public security monitoring, factory building supervision, district public security and the like, and the video monitoring system becomes an indispensable part in the current social life. Especially, with the rapid development of smart cities, smart traffic, smart communities and other fields in recent years, the development of intelligent video analysis technology is promoted.
Most of the existing video monitoring systems acquire video data from front-end equipment, then directly decode and display the video data, and record the decoded pictures into video files only in the background for archiving and later evidence obtaining, and meanwhile, monitoring staff need to continuously watch a screen in a monitoring room to prevent the abnormal events from being saved or stopped in time, but the staff always have fatigue or negligence and cannot find the events in the video in time, even the irretrievable consequences can be caused, so that the development of an intelligent video analysis system is promoted. The intelligent video analysis system directly analyzes the monitoring video pictures shot by the front end of the camera in real time, a user can set a warning area and a rule for triggering alarm according to requirements and purposes, the alarm is given after the analysis result meets the rule, the alarm can be found before things are about to happen or do not happen, effective prevention or prevention can be achieved, meanwhile, the image pictures are stored and kept for evidence obtaining, and the detailed information of the alarm is stored in a database so as to be inquired later. However, real-time intelligent analysis of videos consumes resources, and a video monitoring system usually has tens of or even more videos, so how to simultaneously perform real-time video analysis on a large number of monitored videos has great significance.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide an intelligent video analysis and monitoring system to solve the above-mentioned shortcomings in the background art.
The technical problem solved by the invention is realized by adopting the following technical scheme:
an intelligent video analysis and monitoring system comprises front-end equipment, a video storage server, a video analysis server and a client, wherein the front-end equipment is connected with the video storage server, the video storage server is connected with the video analysis server, and the video analysis server is connected with the client; the front-end equipment comprises a network camera and a switch, the video storage server comprises a video stream acquisition module and a video storage module, and the video analysis server comprises a video data receiving module, a video decoding module, a video analysis module, a video display module, an alarm rule setting module, a database operation module and an alarm information pushing module; the client comprises a historical alarm information query module and an alarm response module; the intelligent video analysis comprises the following specific steps:
1) collecting videos of a monitoring area through a network camera;
2) a video stream acquisition module in the video storage server analyzes an rtsp address from the switch through an FFmpeg tool to acquire a video stream, and then the video storage module stores video data into the video storage server;
3) in the video analysis server, a video analysis module directly analyzes image data stored in a video storage server by using the parallel computing capability of a CUDA (compute unified device architecture) and using an image parallel processing method, and generates an alarm signal if analyzing and judging that the behavior of a foreground target in a warning area meets an alarm condition; meanwhile, storing images for keeping files and obtaining evidence, and storing detailed alarm information (alarm address, alarm time, alarm picture storage position and the like) into a database; after alarming, the alarm information pushing module sends an alarm signal to the client;
the video analysis module analyzes the picture and comprises image preprocessing, background modeling, background updating, target detection, target characteristic value extraction and target analysis:
① preprocessing the decoded image by using image preprocessing techniques such as Gaussian filtering and image enhancement to effectively eliminate irrelevant information in the image, recover useful real information, enhance the detectability of relevant information and simplify data to the utmost extent, thereby improving the reliability of feature extraction, image segmentation, matching and identification;
②, the background model is established by using an online background modeling method based on VIBE, and the model based on the background can be established by only a small amount of background image samples before video analysis, so that the background modeling effectively overcomes the defect of long time consumption of the offline background modeling method;
③ extracting foreground object in motion from the preprocessed image by using the background model established in step ②, and then extracting the characteristic value of the foreground object based on pixels;
④ comparing the feature value extracted in step ③ with the alarm condition set in the warning area for the feature, if the alarm condition is not satisfied then not giving an alarm, if the alarm condition is reached then generating alarm signal and storing the image for evidence collection, at the same time storing the detailed information of alarm (such as alarm time, place, image storage path, etc.) in the database;
4) the background of a network camera monitoring area is dynamic, so that the accuracy rate of intelligent video analysis by using a constant background model is usually low, in order to overcome the difficulty, in the background modeling technology, a background updating strategy is adopted to update the background model in real time to adapt to the change of a dynamic background, when the longitude and latitude of the network camera are constant, if a foreground target is detected to be a moving target all the time after exceeding a certain time range, the foreground target is updated to be the background, the phenomenon of long-time alarm caused by the same target is effectively avoided, and a target which newly generates an alarm can be timely found; when the longitude and latitude of the network camera continuously change in the cruising process, immediately carrying out background modeling again when the network camera is turned to a new position, and establishing a new background model for a new monitoring area;
5) to the video in the video monitoring, adopt the QT to render the technique and can show fifty at least way videos simultaneously, satisfy the demand of carrying out real-time analysis and control simultaneously to dozens of way cameras, simultaneously in order to see the surveillance area more directly perceivedly, utilize QT drawing function to use white polygon to show the frame of surveillance area, if this region produces alarm signal, then use red polygon to show the surveillance area frame to indicate the staff in the vision has alarm information to produce.
In the invention, the network camera is a camera supporting an rtsp protocol, and comprises a gun-shaped camera, a spherical camera and a hemispherical camera, and the camera supporting the holder control function has a cruise function.
Has the advantages that: the invention adopts the CUDA hard decoding technology to effectively reduce the occupancy rate of CPU resources, the CUDA hard decoding technology is realized based on the GPU, so that the decoded video image is stored in the video memory, and then the image is analyzed by using the image parallel processing method based on the CUDA without copying the image from the video memory to the memory, thereby not only reducing the data exchange between the video memory and the memory, but also further reducing the occupancy rate of the CPU resources, and the parallel processing technology accelerates the analysis process, effectively reduces the time consumption of analysis and provides guarantee for realizing real-time analysis.
Drawings
Fig. 1 is a schematic structural diagram of a preferred embodiment of the present invention.
FIG. 2 is a flow chart of alarm rule setting in the preferred embodiment of the present invention.
Fig. 3 is a flow chart of the intelligent video analysis and alarm operation in the preferred embodiment of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained below by combining the specific drawings.
Referring to fig. 1 to 3, an intelligent video analysis and monitoring system includes a front-end device, a video storage server, a video analysis server, and a client, wherein the front-end device is connected to the video storage server, the video storage server is connected to the video analysis server, and the video analysis server is connected to the client; the front-end equipment comprises a network camera and a switch, the video storage server comprises a video stream acquisition module and a video storage module, and the video analysis server comprises a video data receiving module, a video decoding module, a video analysis module, a video display module, an alarm rule setting module, a database operation module and an alarm information pushing module; the client comprises a historical alarm information query module and an alarm response module.
In this embodiment, the network camera is a camera supporting an rtsp protocol, and includes a gun-type camera, a ball-type camera, and a dome-type camera, and the camera supporting the pan-tilt control function has a cruise function.
In this embodiment, in the video storage server, the video stream acquisition module analyzes an rtsp address from the front-end device through an FFmpeg tool to acquire a video stream, and then the video storage module stores video data into the server.
In this embodiment, in the video analysis server, the alarm rule setting module is configured to set an alarm condition for a certain characteristic value in a warning region monitored by the front-end device, the spherical network camera has a cruise function, a plurality of warning regions can be set at different longitude and latitude positions, the alarm condition is independently set in each warning region, and the system performs cruise monitoring on each warning region in different time periods.
In this embodiment, the video data receiving module obtains video data from the video storage server, and then the video decoding module decodes the received video data by using the CUDA hard decoding technology, and the decoded image data is stored in the video memory.
In this embodiment, the video analysis module fully utilizes the parallel computing capability of the CUDA architecture, directly analyzes the image data stored in the video memory by using an image parallel processing method, and generates an alarm signal if analyzing and judging that the behavior of the foreground target in the warning area meets the alarm condition; meanwhile, storing images for keeping files and obtaining evidence, and storing detailed alarm information (alarm address, alarm time, alarm picture storage position and the like) into a database; and after alarming, the alarm information pushing module sends an alarm signal to the client.
In this embodiment, the video display module copies the image data and the alarm related data in the video memory to the memory, and then displays the video image by using the QT rendering technology.
In this embodiment, in the client, the historical alarm information query module is remotely connected to the database, fuzzy search is performed on the alarm detailed information in the database according to the query conditions, and the alarm response module performs corresponding alarm processing, such as voice-controlled alarm and the like, after receiving the alarm signal.
In this embodiment, the intelligent video analysis specifically includes the following steps:
1) the network camera collects videos of a monitoring area;
2) a video stream acquisition module in the video storage server analyzes an rtsp address from front-end equipment through an FFmpeg tool to acquire a video stream, and then the video storage module stores video data into the video storage server;
3) in the video analysis server, a video analysis module directly analyzes image data stored in a video storage server by using the parallel computing capability of a CUDA (compute unified device architecture) and using an image parallel processing method, and generates an alarm signal if analyzing and judging that the behavior of a foreground target in a warning area meets an alarm condition; meanwhile, storing images for keeping files and obtaining evidence, and storing detailed alarm information (alarm address, alarm time, alarm picture storage position and the like) into a database; after alarming, the alarm information pushing module sends an alarm signal to the client;
the video analysis module analyzes the picture and comprises image preprocessing, background modeling, background updating, target detection, target characteristic value extraction and target analysis:
① preprocessing the decoded image by using image preprocessing techniques such as Gaussian filtering and image enhancement to effectively eliminate irrelevant information in the image, recover useful real information, enhance the detectability of relevant information and simplify data to the utmost extent, thereby improving the reliability of feature extraction, image segmentation, matching and identification;
②, the background model is established by using an online background modeling method based on VIBE, and the model based on the background can be established by only a small amount of background image samples before video analysis, so that the background modeling effectively overcomes the defect of long time consumption of the offline background modeling method;
③ extracting foreground object in motion from the preprocessed image by using the background model established in step ②, and then extracting the characteristic value of the foreground object based on pixels;
④ comparing the feature value extracted in step ③ with the alarm condition set in the warning area for the feature, if the alarm condition is not satisfied then not giving an alarm, if the alarm condition is reached then generating alarm signal and storing the image for evidence collection, at the same time storing the detailed information of alarm (such as alarm time, place, image storage path, etc.) in the database;
4) the background of a network camera monitoring area is dynamic, so that the accuracy rate of intelligent video analysis by using a constant background model is usually low, in order to overcome the difficulty, in the background modeling technology, a background updating strategy is adopted to update the background model in real time to adapt to the change of a dynamic background, when the longitude and latitude of the network camera are constant, if a foreground target is detected to be a moving target all the time after exceeding a certain time range, the foreground target is updated to be the background, the phenomenon of long-time alarm caused by the same target is effectively avoided, and a target which newly generates an alarm can be timely found; when the longitude and latitude of the network camera continuously change in the cruising process, immediately carrying out background modeling again when the network camera is turned to a new position, and establishing a new background model for a new monitoring area;
5) to the video in the video monitoring, adopt the QT to render the technique and can show fifty at least way videos simultaneously, satisfy the demand of carrying out real-time analysis and control simultaneously to dozens of way cameras, simultaneously in order to see the surveillance area more directly perceivedly, utilize QT drawing function to use white polygon to show the frame of surveillance area, if this region produces alarm signal, then use red polygon to show the surveillance area frame to indicate the staff in the vision has alarm information to produce.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. An intelligent video analysis and monitoring system comprises front-end equipment, a video storage server, a video analysis server and a client, and is characterized in that the front-end equipment is connected with the video storage server, the video storage server is connected with the video analysis server, and the video analysis server is connected with the client; the front-end equipment comprises a network camera and a switch, the video storage server comprises a video stream acquisition module and a video storage module, and the video analysis server comprises a video data receiving module, a video decoding module, a video analysis module, a video display module, an alarm rule setting module, a database operation module and an alarm information pushing module; the client comprises a historical alarm information query module and an alarm response module; the intelligent video analysis comprises the following specific steps:
1) collecting videos of a monitoring area through a network camera;
2) a video stream acquisition module in the video storage server analyzes an rtsp address from the switch through an FFmpeg tool to acquire a video stream, and then the video storage module stores video data into the video storage server;
3) in the video analysis server, a video analysis module directly analyzes image data stored in a video storage server by using an image parallel processing method by using parallel computing capability of a CUDA (compute unified device architecture), and the image analysis module analyzes pictures and comprises image preprocessing, background modeling, background updating, target detection, target characteristic value extraction and target analysis:
① preprocessing the decoded image by image preprocessing technique to effectively eliminate irrelevant information in the image and recover useful real information;
②, establishing a background model by using an online background modeling method based on VIBE;
③ extracting foreground object in motion from the preprocessed image by using the background model established in step ②, and then extracting the characteristic value of the foreground object based on pixels;
④ comparing the characteristic value extracted in step ③ with the alarm condition set in the warning area for the characteristic, if not, then not alarming, if the alarm condition is reached, then generating alarm signal, storing the image for evidence collection, and storing the detailed alarm information in the database;
4) updating the background of a network camera monitoring area in real time, and updating a foreground target to the background if a foreground target is detected to be a moving target all the time when the longitude and latitude of the network camera is unchanged and exceeds a certain time range; when the longitude and latitude of the network camera continuously change in the cruising process, immediately carrying out background modeling again when the network camera is turned to a new position, and establishing a new background model for a new monitoring area;
5) for videos in video monitoring, a QT rendering technology is adopted to simultaneously display at least fifty videos so as to meet the requirements of simultaneously analyzing and monitoring dozens of network cameras in real time.
2. The intelligent video analysis and monitoring system according to claim 1, wherein in step 5), in order to more visually view the monitored area, the border of the monitored area is displayed by using white polygons by using a QT drawing function, and if an alarm signal is generated in the area, the border of the monitored area is displayed by using red polygons, so as to visually prompt a worker that an alarm message is generated.
3. The intelligent video analysis and monitoring system according to claim 1, wherein the webcam is a webcam supporting rtsp protocol.
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| WO2021142827A1 (en) * | 2020-01-19 | 2021-07-22 | 上海湃道智能科技有限公司 | Video and sensor monitoring integration system |
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| CN112532938B (en) * | 2020-11-26 | 2021-08-31 | 武汉宏数信息技术有限责任公司 | Video monitoring system based on big data technology |
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| CN112486487B (en) * | 2020-11-30 | 2023-05-23 | 中船重工（武汉）凌久电子有限责任公司 | SoC NVR display system-based rapid development method |
| CN112866786A (en) * | 2021-01-14 | 2021-05-28 | 视联动力信息技术股份有限公司 | Video data processing method and device, terminal equipment and storage medium |
| CN113221706B (en) * | 2021-04-30 | 2024-03-22 | 西安聚全网络科技有限公司 | AI analysis method and system for multi-process-based multi-path video stream |
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