CN115866214B - Video accurate management system based on artificial intelligence - Google Patents
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Abstract
The invention discloses an artificial intelligence-based video accurate management system, which comprises a video acquisition unit, a video access unit, a rule setting unit, a video accurate analysis unit, an automatic dispatch unit, an event handling unit, a perception pushing unit and a data storage unit, and relates to the technical field of computers; this accurate management system of administering of video based on artificial intelligence, it is intelligent with ordinary surveillance camera head through setting for the rule, integrate each camera head resource, the accurate analysis unit of video is according to the video early warning rule of setting for, patrol the screen analysis to the camera picture, if find the picture that accords with the early warning rule then carry out the early warning, produce early warning event simultaneously, the early warning event that will produce is automatically dispatched to corresponding staff to handle by automatic dispatch unit, realize unusual action automatic identification, automatic analysis, automatic dispatch full flow processing, and wash the video acquisition unit surface, ensure the definition of video acquisition.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an artificial intelligence-based video accurate management system.
Background
With the development of information technology, particularly the monitoring camera is widely applied to various industries, however, the traditional video acquisition system can only be watched through a specific position, a specific network and a specific computer, and cannot meet the requirements of multi-user, multi-terminal and multi-range watching, and meanwhile, the installed video acquisition camera can only provide simple video browsing and video playback functions, so that early discovery, early disposal and early solving of some emergency events and illegal events occurring in a monitoring area cannot be realized.
Traditional video acquisition system mainly relies on manual browsing to discover illegal events, and operating personnel discovers that the events can only be dispatched and processed through manual mode, and inefficiency, handling untimely problem are prominent, and when video acquisition, the camera in the video acquisition unit can cover the surface at the camera because of being in outdoor state for a long time, therefore can lead to the definition of acquisition video relatively poor when video acquisition.
Disclosure of Invention
The invention aims to solve the problems to at least a certain extent, and by utilizing the existing front-end monitoring camera access system, the ordinary video is intelligentized, the automatic identification, the automatic analysis and the automatic dispatch of abnormal behaviors are realized, the timely discovery, the treatment and the feedback of the abnormal behaviors of a video area are realized with low cost and high availability, the use efficiency of the traditional video acquisition is greatly improved, the surface of a camera in a video acquisition unit is cleaned, and the definition of the video acquisition is ensured.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the video accurate management system based on artificial intelligence comprises a video acquisition unit, a video access unit, a rule setting unit, a video accurate analysis unit, an automatic dispatch unit, an event handling unit, a perception pushing unit and a data storage unit;
the rule setting unit is used for setting a video area early warning rule algorithm, combining a monitoring data set and a setting data set, completely and accurately finding and describing the change condition of a video area through comprehensive analysis and judgment of a plurality of image frames, realizing the purpose of identifying the video area, setting the setting data set to be a fixed image frame input in advance, setting the monitoring data set to be an image frame obtained through real-time monitoring, and adopting a threshold self-adaptive updating method to calculate in order to ensure the accuracy of calculation and the speed of the algorithm by setting early warning threshold values for different monitoring objects;
the video precision analysis unit is used for carrying out inspection analysis on the video accessed by the video access unit in cooperation with a video area early warning rule algorithm, wherein the inspection analysis unit is used for carrying out inspection analysis on each frame of picture of each path of video stream by setting a video data set, then monitoring the state of an area, and then comparing the monitoring data set with the set data set, and immediately carrying out early warning once the numerical value set by the algorithm is found;
the video access unit is responsible for accessing all monitoring videos which are monitored by the camera of the front-end video acquisition unit and support the technical requirements of information transmission, exchange and control in the public safety video acquisition networking system, and converting video streams into other protocols for forwarding;
the automatic dispatch unit is used for automatically dispatching various types of events, the events are automatically dispatched to corresponding staff for processing, and event sources comprise manually uploaded events and events automatically generated by the video precision analysis unit;
the event handling unit is used for receiving, handling and feeding back events, carrying out early warning and supervision on the events which are not handled and fed back in time, marking the events which are not handled and fed back in time and storing the events through the data storage unit.
Preferably, the video acquisition unit is a camera, so that the treatment area is monitored in real time;
the sensing pushing unit can set an automatic sensing user object for the generated early warning event, and timely senses the early warning event to a user in a short message, weChat and system pushing mode;
and a data storage unit for storing data of time and reason of occurrence of the event and storing processing mode and time of the event later.
Preferably, the video area early warning rule algorithm sets a numerical value as
Wherein X is a numerical value set by an algorithm, A 1 To set up the data set, A 2 For monitoring the data set and setting the same data set, the threshold self-adaptive updating method comprises the following steps: by setting up data set A 1 (x, y, z) subtracting the monitoring dataset A 3 (x, y, z) to obtain the same data set A as the monitor data set and the set data set 2 (x, y, z), i.e. the expression +.>
，
Wherein A is 2 When (x, y, z) =1, it indicates that the pixel (x, y) is a motion point, otherwise is a rest point, T (x, y, z) is a differential threshold value for eliminating erroneous judgment possibly caused by noise generated by imaging, and T (x, y, z) is oneThe self-adaptive function is automatically adjusted according to the motion characteristics of the pixels, and the updating process is as follows:
wherein T (x, y, z+1) is an adaptive update result; alpha is an update coefficient, I (x, y, z) is a gray value of an image, M (x, y, z) is a set data set matching operator, and the set data set matching operator only takes two values of 0 and 1; extracting the background from the monitoring data set, and obtaining the updating result of the background according to the sequence frame, the previous background frame and the updating coefficient in the updating process, wherein the updating result is as follows:
，
where R (x, y, z) is used to count the number of times a pixel (x, y) remains unchanged for two consecutive frames, if the pixel is different from the background but has remained unchanged for a long time for two consecutive frames, then the object at that pixel enters the field of view and comes to rest, should be treated as the background, where snmm is the threshold of whether or not to rest, and snmm=36.
Preferably, the video is accessed according with two modes of GB/T28181-2011 protocol and GB/T28181-2016 protocol, and the video stream forwarding format can realize RTSP protocol, FLV protocol, HLS protocol and RTMP protocol conversion.
Preferably, the video area early warning rule algorithms analyze the accessed video in a serial or serial-parallel mode; the monitoring type of the video area early warning rule algorithm comprises illegal parking of a fixed point area, stacking of disordered piles, disordered parking of a non-motor vehicle and out-of-store operation.
Preferably, the artificial intelligent video accurate management system further comprises a mobile phone mobile terminal APP.
Preferably, the mobile terminal APP of the mobile phone may be any one of a mobile phone and a tablet.
Advantageous effects
The invention provides an artificial intelligence-based video accurate management system, which has the following beneficial effects compared with the prior art:
1. according to the video accurate management system based on artificial intelligence, all videos conforming to GB28181 protocol are accessed into the system through the video access unit, the GB28181 protocol video can be converted into RTSP protocol, FLV protocol, HLS protocol and RTMP protocol, video data can be stored, converted and distributed, and cameras of different manufacturers can be managed in the same platform.
2. The video accurate management system based on artificial intelligence can set an early warning rule for images in a video area through the rule setting unit, can early warn events, can perform early warning in places such as illegal parking, disordered material stacking, non-motor vehicle disordered parking and out-of-store operation in a fixed point area in the aspect of social management, and can intelligently integrate various camera resources through the setting rule when the placing positions of vehicles, materials and the like are located outside the fixed point area.
3. According to the video accurate management system based on artificial intelligence, the video accurate analysis unit is used for carrying out inspection analysis on the camera picture according to the set video early warning rules, if the picture meeting the early warning rules is found, early warning is carried out, meanwhile, an early warning event is generated, the generated early warning event is automatically distributed to corresponding staff by the automatic dispatch unit for processing, automatic recognition of abnormal behaviors is achieved, automatic analysis is carried out, and full-flow processing is automatically dispatched.
Drawings
FIG. 1 is a block diagram of an artificial intelligence video precision governance system;
FIG. 2 is a flow chart of an artificial intelligence video precision governance system;
FIG. 3 is a schematic diagram of the overall device of a video acquisition unit of the artificial intelligent video precision control system;
FIG. 4 is a schematic diagram of the internal devices of a video acquisition unit of the artificial intelligent video precision control system;
FIG. 5 is a schematic diagram of a cleaning mechanism in a video acquisition unit of the artificial intelligent video precision control system;
FIG. 6 is a schematic view of a part of a cleaning mechanism in a video acquisition unit of the artificial intelligent video precision control system;
FIG. 7 is a schematic diagram of a part of a water spraying mechanism in a video acquisition unit of the artificial intelligent video accurate control system.
In the figure: 1. a camera; 2. a base; 3. a water spraying mechanism; 301. a water inlet pipe; 302. an extension plate; 303. a baffle; 304. a water jet; 4. a driving mechanism; 401. driving a first motor; 402. a cross universal shaft; 403. a first ball sleeve; 404. a telescopic rod; 405. a toothed plate I; 406. a first gear; 407. a fixed rod; 408. a second ball sleeve; 409. a second gear; 410. a toothed plate II; 411. a second driving motor; 412. an extension rod; 5. a cleaning mechanism; 501. a side plate; 502. a first chute; 503. a first sliding block; 504. a toothed plate III; 505. a collar; 506. a second chute; 507. a two-way screw rod; 508. a second slide block; 509. an extension block; 510. a double-sided squeegee; 511. and a third gear.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
Examples
Referring to fig. 1-2, the video accurate management system based on artificial intelligence comprises a video acquisition unit, a video access unit, a rule setting unit, a video accurate analysis unit, an automatic dispatch unit, an event handling unit, a perception pushing unit and a data storage unit;
the video acquisition unit is a camera, so that the treatment area is monitored in real time;
the video access unit is responsible for accessing all monitoring videos which are monitored by the cameras of the front-end video acquisition unit and support the technical requirements of information transmission, exchange and control in a public safety video acquisition networking system, the technical requirements of information transmission, exchange and control of the public safety video acquisition networking system are GB28181 protocol, video streams are converted into other protocols to be forwarded, the front-end equipment is a camera which supports different manufacturers, the video access unit uses ZLMMediakit streaming media service to be responsible for accessing the media streams of the front-end equipment, and the ZLMMediakit streaming media service supports linux, windows, ios, android full platform, so that the video data can be stored, converted and distributed, and the cameras of different manufacturers are managed in the same platform;
the rule setting unit is used for setting a video area early warning rule algorithm, and the video area early warning rule algorithm sets a numerical value as
Wherein X is a numerical value set by an algorithm, A 1 To set up the data set, A 2 In order to monitor the same data set as the set data set, combine the set data set with the set data set, through carrying on comprehensive analysis and judgement to a plurality of image frames, and find and describe the change situation of the video area completely, accurately, realize the goal of distinguishing the video area, set the data set as the fixed image frame input in advance, monitor the data set as the image frame that the real-time monitoring gets, through setting up the early warning threshold value to different monitoring objects, in order to guarantee the accuracy of calculating and speed of the algorithm, the system adopts the adaptive updating method of threshold value, through setting up the data set A 1 (x, y, z) subtracting the monitoring dataset A 3 (x, y, z) to obtain the same data set A as the monitor data set and the set data set 2 (x, y, z), i.e. the expression +.>
，
Wherein A is 2 When (x, y, z) =1, it indicates that the pixel (x, y) is a motion point, otherwise, it is a rest point, T (x, y, z) is a differential threshold, for eliminating erroneous judgment possibly caused by noise points generated due to imaging, and T (x, y, z) is an adaptive function, and is automatically adjusted according to the motion characteristics of the pixel, and the updating process is as follows:
wherein T (x, y, z+1) is adaptiveUpdating a result; alpha is an update coefficient, I (x, y, z) is a gray value of an image, M (x, y, z) is a set data set matching operator, and the set data set matching operator only takes two values of 0 and 1; extracting the background from the monitoring data set, and obtaining the updating result of the background according to the sequence frame, the previous background frame and the updating coefficient in the updating process, wherein the updating result is as follows:
，
wherein R (x, y, z) is used to count the number of times that a pixel (x, y) remains unchanged during two consecutive frames, if the pixel is different from the background, but remains unchanged for a long time during two consecutive frames, then the object at the pixel enters the field of view and stands still, and should be treated as a background, wherein snm is a threshold value for measuring whether to stand still, and snm=36, as can be seen from the above formula, the above background extraction method considers both the initial background of the image and the gradual change that may occur in the field of view, and also considers the situation that an object may move into the field of view and stand still, etc., so that the method can extract the background image relatively accurately, and intelligent the common monitoring camera by setting rules, and integrating the camera resources;
the video precision analysis unit is used for carrying out inspection analysis on videos accessed by the video access unit by matching with a video area early warning rule algorithm, setting a video data set, monitoring the state of an area, comparing each frame of picture of each path of video stream by comparing the monitoring data set with the set data set, immediately early warning when the numerical percentage value set by the algorithm is found, operating in an asynchronous multithreading mode, carrying out inspection analysis on the picture of a camera according to the set video early warning rule, carrying out early warning when the picture conforming to the early warning rule is found, generating early warning events at the same time, automatically dispatching the generated early warning events to corresponding staff by the automatic dispatching unit for processing, and realizing automatic identification, automatic analysis and automatic dispatching of the abnormal behavior;
the automatic dispatch unit is used for automatically dispatching various types of events, the events are automatically dispatched to corresponding staff for processing, and event sources comprise manually uploaded events and events automatically generated by the video analysis unit;
the event handling unit is used for receiving, handling and feeding back events, carrying out early warning and supervision on the events which are not handled and fed back in time, marking the events which are not handled and fed back in time and storing the events through the data storage unit;
the sensing pushing unit can set an automatic sensing user object for the generated early warning event, and timely senses the early warning event to a user in a short message, weChat and system pushing mode;
the data storage unit stores data of time and reason of occurrence of the event, and stores processing mode and time of the event afterwards.
In this embodiment, the video is accessed according to two modes of GB/T28181-2011 protocol and GB/T28181-2016 protocol, and the video stream forwarding format can realize RTSP protocol, FLV protocol, HLS protocol and RTMP protocol conversion.
In the embodiment, the video area early warning rule algorithm adopts a serial or serial-parallel connection mode to analyze the accessed video; the monitoring type of the video area early warning rule algorithm comprises illegal parking of a fixed-point area, stacking of random piles, random parking of non-motor vehicles and out-of-store operation.
In this embodiment, the artificial intelligent video accurate governance system further includes a mobile phone mobile terminal APP.
In this embodiment, the mobile phone APP may be any one of a mobile phone and a tablet.
Examples
Referring to fig. 3-7, the video capturing unit includes a camera 1 and a base 2, a water spraying mechanism 3 is disposed on one side of the base 2, a driving mechanism 4 is installed on one side of the camera 1, a cleaning mechanism 5 is disposed on one side of the driving mechanism 4, the driving mechanism 4 includes a first driving motor 401 fixed inside the base 2, a cross universal shaft 402 is fixedly connected to one side of the first driving motor 401, ball sleeves first 403 are respectively sleeved at two corresponding ends of the cross universal shaft 402, ball sleeves second 408 are respectively sleeved at the other two ends of the cross universal shaft 402, one end of the ball sleeves second 408 is fixedly connected with a fixing rod 407, the fixing rod 407 is fixedly connected with the camera 1, the cross universal shaft 402 is fixedly connected to one side of the first driving motor 401, a gear first 406 is connected to one side of the gear first 406 in an engaged manner, a telescopic rod 404 is fixedly connected to one side of the toothed plate first 405, one side of the telescopic rod 404 is fixedly connected to a second driving motor 411, one side of the cross universal shaft 402, a second 409 is fixedly connected to one side of the second driving motor 411, and one side of the toothed plate 410 is fixedly connected to one side of the second toothed plate 410.
In this embodiment, the cleaning mechanism 5 includes a side plate 501 fixed on one side of the camera 1, a first sliding chute 502 is formed on one side of the side plate 501, a first sliding block 503 is slidably connected in the first sliding chute 502, a third toothed plate 504 is fixedly connected on one side of the first sliding block 503, an extension rod 412 is fixedly connected on the same side of the first sliding block 503 and the third toothed plate 504, a collar 505 is sleeved on the outer surface of the extension rod 412, a second sliding chute 506 is formed on one end of the side plate 501, a bidirectional screw 507 is rotatably connected in the second sliding chute 506, a second sliding block 508 is slidably connected on the outer surface of the bidirectional screw 507, an extension block 509 is fixedly connected on one side of the second sliding block 508, a double-sided scraping plate 510 is clamped on one side of the extension block 509, a third gear 511 is fixedly connected on one side surface of the double-sided scraping plate 510, dust and water are scraped off by the upper surface of the double-sided scraping plate 510, and then water on the surface of the camera 1 is scraped by the lower surface, and the operation is cleaned in the rotation process of the camera 1, the video of the camera 1 is not affected, and the video is clearly collected by cleaning and guaranteeing the video.
In this embodiment, the water spraying mechanism 3 includes a water inlet pipe 301 fixed on one side of the base 2, one end of the water inlet pipe 301 is provided with a plurality of water spraying ports 304, one side of the water spraying ports 304 is abutted with a baffle 303, one side of the baffle 303 is fixedly connected with an extension plate 302, one side of the extension plate 302 is fixed on a toothed plate three 504, the extension plate 302 is controlled to drive the baffle 303 to move, and through holes on the baffle 303 can expose the water spraying ports when the bidirectional scraper 510 ascends, and the bidirectional scraper 510 is adapted to work of the bidirectional scraper 510 and can flush dust on the bidirectional scraper 510.
The video accurate management and application working principle based on artificial intelligence comprises the following specific steps:
s1: and (3) video acquisition: the video acquisition unit acquires video of the monitoring area through the camera;
s2: front-end video access: the video access unit is used for accessing the front-end video acquisition into the system;
s3: setting a rule algorithm: the rule setting unit sets a video area early warning rule algorithm and dynamically sets an early warning threshold;
s4: video inspection analysis: the video precision analysis unit applies a rule algorithm to carry out inspection analysis on the accessed video to generate early warning information;
s5: early warning event processing: aiming at the generated early warning information, the automatic dispatch unit automatically dispatches the event to corresponding staff for processing;
s6: untreated event supervision: the event handling unit provides events which are not handled and fed back in time for staff to perform early warning and supervision;
s7: event push user: the sensing pushing unit timely senses the generated early warning event to a user.
The working principle of the video acquisition unit is as follows: the base 2 fixes the position of the camera 1, the first driving motor 401 rotates to drive the cross universal shaft 402 to rotate through the ball sleeve 403, the supine angle of the camera 1 is adjusted, then the second driving motor 411 controls the cross universal shaft 402 to rotate, the camera 1 is controlled to swing left and right, meanwhile, the second gear 409 controls the position of the toothed plate 410 to move, the toothed plate 410 moves to control the sliding block 503 to move in the sliding groove 502 through the extension rod 412, the third gear 504 controls the gear 511 to rotate, then the bidirectional screw 507 is controlled to rotate, the sliding block 508 is controlled to slide on the bidirectional screw 507, in the moving process of the toothed plate three 504, the extending plate 302 is controlled to move, then the position of the baffle 303 is controlled to move, through holes in the extending plate 302 are communicated with the water spraying ports 304 at intervals, water flows to the surface of the camera 1 through the water inlet pipe 301 and the water spraying ports 304, then the double-sided scraper 510 scrapes dust mixed water on the surface of the camera 1 upwards, the double-sided scraper 510 moves downwards to dry the water on the surface of the camera 1, and meanwhile, the double-sided scraper 510 slides down the mixture to the double-sided scraper 510 to swing to the position of the camera 1 through the water spraying plate 410 when the double-sided scraper 510 is not meshed with the toothed plate 1, and the camera 1 swings to the left and the camera 409 is not meshed with the left and right.
And all that is not described in detail in this specification is well known to those skilled in the art.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. Video accurate management system based on artificial intelligence, its characterized in that: the system comprises a video acquisition unit, a video access unit, a rule setting unit, a video precision analysis unit, an automatic dispatch unit, an event handling unit, a perception pushing unit and a data storage unit;
the rule setting unit is used for setting a video area early warning rule algorithm, combining a monitoring data set with a setting data set, comprehensively analyzing and judging a plurality of image frames, completely and accurately finding and describing the change condition of a video area, setting the data set as a fixed image frame input in advance, setting the monitoring data set as an image frame obtained by real-time monitoring, and adopting a threshold self-adaptive updating method to calculate in order to ensure the accuracy of calculation and the speed of the algorithm by setting early warning threshold values for different monitoring objects;
the video precision analysis unit is used for carrying out inspection analysis on the video accessed by the video access unit in cooperation with a video area early warning rule algorithm, wherein the inspection analysis unit is used for carrying out inspection analysis on each frame of picture of each path of video stream by setting a video data set, then monitoring the state of an area, and then comparing the monitoring data set with the set data set, and immediately carrying out early warning once the numerical value set by the algorithm is found;
the video access unit is responsible for accessing all monitoring videos which are monitored by the camera of the front-end video acquisition unit and support the technical requirements of information transmission, exchange and control in the public safety video acquisition networking system, and converting video streams into other protocols for forwarding;
the automatic dispatch unit is used for automatically dispatching various types of events, the events are automatically dispatched to corresponding staff for processing, and event sources comprise manually uploaded events and events automatically generated by the video precision analysis unit;
the event handling unit is used for receiving, handling and feeding back events, carrying out early warning and supervision on the events which are not handled and fed back in time, marking the events which are not handled and fed back in time and storing the events through the data storage unit;
the video area early warning rule algorithm sets a numerical value as
Wherein X is a numerical value set by an algorithm, A 1 To set up the data set, A 2 For monitoring the data set and setting the same data set, the threshold self-adaptive updating method comprises the following steps: by setting up data set A 1 (x, y, z) subtracting the monitoring dataset A 3 (x, y, z) to obtain the same data set A as the monitor data set and the set data set 2 (x, y, z), i.e. the expression +.>
，
Wherein A is 2 When (x, y, z) =1, it indicates that the pixel (x, y) is a motion point, otherwise, it is a rest point, T (x, y, z) is a differential threshold, for eliminating erroneous judgment possibly caused by noise points generated due to imaging, and T (x, y, z) is an adaptive function, and is automatically adjusted according to the motion characteristics of the pixel, and the updating process is as follows:
wherein T (x, y, z+1) is an adaptive update result; alpha is an update coefficient, I (x, y, z) is a gray value of an image, M (x, y, z) is a set data set matching operator, and the set data set matching operator only takes two values of 0 and 1; extracting the background from the monitoring data set, and obtaining the updating result of the background according to the sequence frame, the previous background frame and the updating coefficient in the updating process, wherein the updating result is as follows:
，
where R (x, y, z) is used to count the number of times a pixel (x, y) remains unchanged for two consecutive frames, if the pixel is different from the background but has remained unchanged for a long time for two consecutive frames, then the object at that pixel enters the field of view and comes to rest, should be treated as the background, where snmm is the threshold of whether or not to rest, and snmm=36.
2. The artificial intelligence based video precision governance management system of claim 1, wherein: the video acquisition unit is a camera, so that the treatment area is monitored in real time;
the sensing pushing unit can set an automatic sensing user object for the generated early warning event, and timely senses the early warning event to a user in a short message, weChat and system pushing mode;
and a data storage unit for storing data of time and reason of occurrence of the event and storing processing mode and time of the event later.
3. The artificial intelligence based video precision governance management system of claim 1, wherein: the video is accessed according with two modes of GB/T28181-2011 protocol and GB/T28181-2016 protocol, and the video stream forwarding format can realize RTSP protocol, FLV protocol, HLS protocol and RTMP protocol conversion.
4. The artificial intelligence based video precision governance management system of claim 1, wherein: analyzing an access video in a serial or serial-parallel mode among the video region early warning rule algorithms; the monitoring type of the video area early warning rule algorithm comprises illegal parking of a fixed point area, stacking of disordered piles, disordered parking of a non-motor vehicle and out-of-store operation.
5. The artificial intelligence based video precision governance management system of claim 1, wherein: the artificial intelligent video accurate management system further comprises a mobile phone mobile terminal APP.
6. The artificial intelligence based video precision governance management system of claim 5, wherein: the mobile terminal APP of the mobile phone can be any one of a mobile phone and a tablet.
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| CN112087604A (en) * | 2020-09-18 | 2020-12-15 | 浪潮天元通信信息系统有限公司 | Intelligent monitoring video management and control method based on image recognition |
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| CN103198605A (en) * | 2013-03-11 | 2013-07-10 | 成都百威讯科技有限责任公司 | Indoor emergent abnormal event alarm system |
| CN114302103A (en) * | 2021-10-29 | 2022-04-08 | 四川天翼网络服务有限公司 | Intelligent monitoring operation and maintenance system |
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