CN112906447A - Video monitoring high-risk area-based abnormal event detection system - Google Patents
Video monitoring high-risk area-based abnormal event detection system Download PDFInfo
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- CN112906447A CN112906447A CN202011001289.0A CN202011001289A CN112906447A CN 112906447 A CN112906447 A CN 112906447A CN 202011001289 A CN202011001289 A CN 202011001289A CN 112906447 A CN112906447 A CN 112906447A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
Abstract
The invention relates to the technical field of safety detection, and provides an abnormal event detection system based on a video monitoring high-risk area, which comprises: the system comprises a video monitoring module, a cloud server and an alarm module; the video monitoring module is used for carrying out real-time video monitoring on the field environment; the cloud server is used for inputting the acquired first image to be detected into a detection model which is trained in advance, performing convolution processing on the first image based on the detection model, determining the position information of the detection frame of each abnormal event, and then determining whether the abnormal event exists in the target object according to the position information of the detection frame of the target object and the position information of the detection frame of the abnormal event; the alarm module is used for alarming when the data are abnormal. The rationality of the urban video monitoring layout is evaluated, the layout of cameras in an urban video monitoring system is improved, and no dead angle is monitored.
Description
Technical Field
The invention relates to the technical field of safety detection, in particular to an abnormal event detection system based on a video monitoring high-risk area.
Background
Along with the promotion of industrialization and the promotion of production and living technology, the degree of automation of each item production is higher and higher, need real-time collection field data and field device running state to help the better production action control adjustment of remote system many times, but some high-risk scene such as the high factory of dust concentration, transformer substation and petrochemical industry production scene, manual monitoring is very dangerous and detect the precision not enough, and present most data acquisition equipment function singleness, and the adaptability is relatively poor, can not carry out the function selection according to user's demand, it is very inconvenient.
In addition, road traffic accidents bring great harm to lives and properties of people, and more than one hundred thousand people die every year. Among these, many times are road traffic accidents caused by abnormal events of drivers. The abnormal events of the driver include no call, smoking, no safety belt fastening and the like. In order to reduce the occurrence of road traffic accidents, it is necessary to accurately detect the abnormal events of the driver in time.
In summary, how to provide a high-risk environment monitoring device which can be used for high-risk environment field monitoring, reduces the risk of manual operation, and has good adaptability is a problem to be solved by technical personnel in the field.
Disclosure of Invention
The invention provides an abnormal event detection system based on a video monitoring high-risk area, which solves the technical problems.
The invention provides an abnormal event detection system based on video monitoring high-risk areas for solving the technical problems, which comprises: the system comprises a video monitoring module, a cloud server and an alarm module;
the video monitoring module is used for carrying out real-time video monitoring on an on-site environment, and comprises a camera, a holder and a protective outer cover, wherein the camera is a wireless infrared high-definition camera, the camera is connected with the holder, the video monitoring module is connected with the cloud server to send video information to the cloud server, and a monitoring center can access the cloud server to obtain related video information;
the cloud server comprises a first determining module and a second determining module, wherein the first determining module is used for inputting an acquired first image to be detected into a detection model which is trained in advance, and performing convolution processing on the first image based on the detection model to determine a first feature map; determining the position information of the detection frame of each target object in the first characteristic diagram and the position information of the detection frame of each abnormal event;
a second determining module, configured to determine, for each target object and each abnormal event, an intersection ratio between the target object and the detection frame of the abnormal event according to the position information of the detection frame of the target object and the position information of the detection frame of the abnormal event, and determine whether the target object has the abnormal event according to the intersection ratio;
the alarm module is used for alarming when the data are abnormal.
Preferably, the alarm module comprises a field alarm unit and a remote alarm module, the field alarm unit comprises a voice synthesis device and a loudspeaker, the loudspeaker is connected with the central control mainboard through the voice synthesis device, and the remote alarm module is used for carrying out automatic dialing alarm and abnormal data transmission on the monitoring center.
Preferably, the second determining module is configured to calculate, based on the acquired road network data and the multidimensional city basic data, a high risk degree grade corresponding to a node in the road network data according to the multidimensional city basic data within a set range centered on the node in the road network data.
Preferably, the video monitoring module is further configured to optimize the position and the visible area of the camera around the node in each road network data based on the position information and the visible area information of the camera in the video monitoring network, so as to meet the coverage requirement corresponding to the node in each road network data.
Preferably, the video monitoring module comprises a plurality of sub video monitoring modules which are distributed on a map and are correspondingly networked with the cloud server.
Preferably, the second determining module is further configured to perform early warning analysis on the abnormal event, and search for correlation of the abnormal event in space and time; establishing an abnormal behavior database through the abnormal behavior alarm information received by the front-end video monitoring equipment; the abnormal behavior database provides metadata related to the event for subsequent analysis, then correlation analysis is carried out on the single-point historical data, and the time autocorrelation of the single-point historical data is analyzed; when a monitoring point detects that abnormal behaviors occur, correlation analysis with historical data in the database is started.
Preferably, the second determining module is further configured to classify the monitoring targets according to the level of risk assessment, and set a corresponding abnormal behavior for each type of monitoring target; and constructing a risk evaluation model according to the established abnormal behavior database, and endowing each type of behavior with a risk weight according to the potential safety voyage consequences of the abnormal behavior.
Has the advantages that: the invention provides an abnormal event detection system based on video monitoring of a high-risk area, which comprises: the system comprises a video monitoring module, a cloud server and an alarm module; the video monitoring module is used for carrying out real-time video monitoring on the field environment; the cloud server is used for inputting the acquired first image to be detected into a detection model which is trained in advance, performing convolution processing on the first image based on the detection model, determining the position information of the detection frame of each target object in the first characteristic diagram and the position information of the detection frame of each abnormal event, then determining the intersection and parallel ratio of the target object and the detection frame of the abnormal event according to the position information of the detection frame of the target object and the position information of the detection frame of the abnormal event, and determining whether the target object has the abnormal event or not according to the intersection and parallel ratio; the alarm module is used for alarming when the data are abnormal. The rationality of the urban video monitoring layout is evaluated, the layout of cameras in an urban video monitoring system is improved, and no dead angle is monitored.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of an abnormal event detection system based on video monitoring in a high-risk area according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention. The invention is described in more detail in the following paragraphs by way of example with reference to the accompanying drawings. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
As shown in fig. 1, the present invention provides an abnormal event detection system based on video monitoring of a high-risk area, which is characterized by comprising: the system comprises a video monitoring module, a cloud server and an alarm module;
the video monitoring module is used for carrying out real-time video monitoring on an on-site environment, and comprises a camera, a holder and a protective outer cover, wherein the camera is a wireless infrared high-definition camera, the camera is connected with the holder, the video monitoring module is connected with the cloud server to send video information to the cloud server, and a monitoring center can access the cloud server to obtain related video information;
the cloud server comprises a first determining module and a second determining module, wherein the first determining module is used for inputting an acquired first image to be detected into a detection model which is trained in advance, and performing convolution processing on the first image based on the detection model to determine a first feature map; determining the position information of the detection frame of each target object in the first characteristic diagram and the position information of the detection frame of each abnormal event;
a second determining module, configured to determine, for each target object and each abnormal event, an intersection ratio between the target object and the detection frame of the abnormal event according to the position information of the detection frame of the target object and the position information of the detection frame of the abnormal event, and determine whether the target object has the abnormal event according to the intersection ratio;
the alarm module is used for alarming when the data are abnormal.
The detection system comprises a front-end detection module, a video monitoring module, a cloud server, an alarm module and a central control mainboard arranged in the shell.
The casing includes cloud platform base and sets up the column main part on the universal wheel, and cloud platform pedestal mounting is in the upper end of casing, and the column main part is installed to the lower extreme of casing, and the universal wheel is installed to the lower extreme of column main part, and the column main part is the cavity cylindric. The cylinder wall of the columnar main body is provided with a plurality of heat dissipation holes and a heat dissipation fan, the heat dissipation fan is installed on the upper portion of the cylinder body of the columnar main body, the upper end of the cylinder body of the columnar main body is communicated with the inside of the shell, and the heat dissipation fan supplies air into the shell to dissipate heat in the shell when the central control mainboard works.
The heat radiation fan is electrically connected with the central control mainboard. And the central control mainboard includes the core controller, optical coupling isolation module, the power supply unit, ZIGBEE receiving module and display device, optical coupling isolation module and display device all are connected with the core controller electricity, the core controller adopts the model to be STM32F103 chip, STM32 series singlechip performance is high, the low power dissipation, the STM32 singlechip clock frequency of enhancement mode can reach 72Mz, 11 timers have, high speed has, the computing power of high accuracy, be the higher treater of neutral price of like product, zigBee receives receiving module CC 2530.
The front end detection module is used for detecting relevant parameter information in the environment, and send parameter information to the central control mainboard, the front end detection module includes wireless sensing module and a plurality of monitor, the front end detection module is connected with the central control mainboard electricity, and wireless sensing module includes ZIGBEE sending module, AD converter and sensor group, the sensor group is connected with ZIGBEE sending module through the AD converter, the sensor group includes harmful gas sensor, temperature and humidity sensor, smoke transducer, one or more combinations among PM2.5 sensor and the pressure sensor. The plurality of monitors include bolometers, dust meters, noise meters, and voltage monitors.
The video monitoring module is used for carrying out real-time video monitoring on the site environment, and the video monitoring module comprises a camera, a holder and a protective housing, wherein the camera is a wireless infrared high-definition camera, the camera is connected with the holder, the video monitoring module is connected with a cloud server to send video information to the cloud server, the monitoring center can access the cloud server to acquire relevant video information, and the cloud server is electrically connected with the central control mainboard.
The system can also be a bayonet device arranged at an intersection, can acquire video information of a vehicle passing through the intersection, can acquire a vehicle image in the video by using a vehicle detector, and needs to intercept a window partial image in the vehicle image if abnormal behaviors of a driver or a co-driver in the window need to be detected, such as the driver does not tie a safety belt, the driver makes a call and the like, wherein the window partial image is a first image to be detected; if abnormal behaviors of the vehicle need to be detected, such as turning on a high beam, the image of the vehicle is directly used as a first image to be detected. The process of acquiring the first image to be detected belongs to the prior art, and the process is not performed redundantly.
The method comprises the steps that a detection model which is trained in advance is stored in the electronic equipment, after the electronic equipment acquires a first image to be detected, the first image is input into the detection model, the detection model comprises a plurality of convolutional layers, the first image can be subjected to convolution processing based on the convolutional layers in the detection model, a first feature map can be determined, and the first feature map comprises position information of a detection frame of each target object and position information of a detection frame of each abnormal behavior.
The target objects comprise a driver, a co-driver, a car lamp and the like, and the abnormal behaviors comprise call receiving and making, smoking, unbuckled safety belts, high beam opening and the like. The detection frame may be a rectangular detection frame, and the position information of the detection frame may be coordinate information of four vertices of the detection frame in the image coordinate system, or may be coordinate information of one vertex of the detection frame and the length and width of the detection frame. In the embodiment of the present invention, the position information of the detection frame is not specifically limited as long as the position area of the detection frame in the first image can be marked.
Preferably, the first basic feature map is input into a classification subnet of the detection model, and the first feature map is input into the classification subnet; processing the first characteristic diagram and the first basic characteristic diagram based on the convolution layer and the full-connection layer in the classification subnet, and determining the confidence coefficient of the target object with the abnormal behavior; and judging whether the confidence coefficient is greater than a preset third threshold value, and if so, triggering a second determining module.
For each second image, inputting the second image and a calibration image corresponding to the second image into a detection model; the calibration image comprises position information of a detection frame of each target object, position information of a detection frame of each abnormal behavior, and identification information of whether the target object has the abnormal behavior or not aiming at each target object and each abnormal behavior; determining a second basic feature map based on a main network of a detection model, and respectively inputting the second basic feature map into a detection subnet and a classification subnet of the detection model; performing convolution processing on the second basic feature map based on the detection subnet, determining a fifth feature map, position information of a detection frame of each target object in the fifth feature map and position information of a detection frame of each abnormal behavior, and inputting the fifth feature map into the classification subnet; processing the fifth characteristic diagram and the second basic characteristic diagram based on the convolution layer and the full-connection layer in the classification subnet, and determining the confidence coefficient of the target object with the abnormal behavior; and for each target object and each abnormal behavior, determining the intersection and combination ratio of the detection frame of the target object and the detection frame of the abnormal behavior, determining the confidence of the target object having the abnormal behavior according to the intersection and combination ratio of the detection frame of the target object and the detection frame of the abnormal behavior, and for the calibration image corresponding to the second image, determining the intersection and combination ratio of the detection frame of the target object and the detection frame of the abnormal behavior, and training the detection model according to the identification information of whether the target object has the abnormal behavior in the calibration image.
Preferably, the video monitoring module is further configured to optimize the position and the visible area of the camera around the node in each road network data based on the position information and the visible area information of the camera in the video monitoring network, so as to meet the coverage requirement corresponding to the node in each road network data. According to the high-risk degree grade corresponding to the nodes in the road network data, corresponding coverage requirements are set for the nodes in each road network data, and only one implementation mode is listed. Coverage requirements can be further enhanced according to the specific requirements of the video monitoring system. For example: the 50 m range of the node attachment is required to be completely covered by the visual field of the camera, or the cross coverage of two cameras is required to be minimum. According to the different coverage requirements, different adjustments are performed during the subsequent optimization, which is not described herein again.
In an optimal scheme, for the high-risk value P corresponding to the node, in addition to the above formula, the calculation method can also set different weighted values for different cases according to the case grades to comprehensively calculate the high-risk value P corresponding to the node. For example, the criminal case has the highest grade, the set weighting value is the largest, for example, 1, that is, as long as the criminal case occurs, the monitoring is necessarily required to be intensively performed. The invention is not limited to a specific calculation method of the high risk value P corresponding to the node. Meanwhile, the invention is not limited to the types of the adopted city basic data, and the more the city basic data can be utilized, the better the layout optimization result is.
Has the advantages that: the invention provides an abnormal event detection system based on video monitoring of a high-risk area, which comprises: the system comprises a video monitoring module, a cloud server and an alarm module; the video monitoring module is used for carrying out real-time video monitoring on the field environment; the cloud server is used for inputting the acquired first image to be detected into a detection model which is trained in advance, performing convolution processing on the first image based on the detection model, determining the position information of the detection frame of each target object in the first characteristic diagram and the position information of the detection frame of each abnormal event, then determining the intersection and parallel ratio of the target object and the detection frame of the abnormal event according to the position information of the detection frame of the target object and the position information of the detection frame of the abnormal event, and determining whether the target object has the abnormal event or not according to the intersection and parallel ratio; the alarm module is used for alarming when the data are abnormal. The rationality of the urban video monitoring layout is evaluated, the layout of cameras in an urban video monitoring system is improved, and no dead angle is monitored.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; the present invention may be readily implemented by those of ordinary skill in the art as illustrated in the accompanying drawings and described above; however, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the scope of the invention as defined by the appended claims; meanwhile, any changes, modifications, and evolutions of the equivalent changes of the above embodiments according to the actual techniques of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (7)
1. An abnormal event detection system based on video monitoring high-risk areas is characterized by comprising: the system comprises a video monitoring module, a cloud server and an alarm module;
the video monitoring module is used for carrying out real-time video monitoring on an on-site environment, and comprises a camera, a holder and a protective outer cover, wherein the camera is a wireless infrared high-definition camera, the camera is connected with the holder, the video monitoring module is connected with the cloud server to send video information to the cloud server, and a monitoring center can access the cloud server to obtain related video information;
the cloud server comprises a first determining module and a second determining module, wherein the first determining module is used for inputting an acquired first image to be detected into a detection model which is trained in advance, and performing convolution processing on the first image based on the detection model to determine a first feature map; determining the position information of the detection frame of each target object in the first characteristic diagram and the position information of the detection frame of each abnormal event;
a second determining module, configured to determine, for each target object and each abnormal event, an intersection ratio between the target object and the detection frame of the abnormal event according to the position information of the detection frame of the target object and the position information of the detection frame of the abnormal event, and determine whether the target object has the abnormal event according to the intersection ratio;
the alarm module is used for alarming when the data are abnormal.
2. The system for detecting the abnormal events based on the video monitoring high-risk areas as claimed in claim 1, wherein the alarm module comprises a field alarm unit and a remote alarm module, the field alarm unit comprises a voice synthesis device and a loudspeaker, the loudspeaker is connected with the central control mainboard through the voice synthesis device, and the remote alarm module is used for carrying out automatic dialing alarm and abnormal data transmission to a monitoring center.
3. The system for detecting the abnormal events based on the video surveillance high-risk areas as claimed in claim 1, wherein the second determining module is configured to calculate the high-risk degree grade corresponding to the node in the road network data according to the multi-dimensional city basic data within a set range centered on the node in the road network data based on the acquired road network data and the multi-dimensional city basic data.
4. The system for detecting the abnormal events based on the video surveillance high-risk areas as claimed in claim 1, wherein the video surveillance module is further configured to optimize the position and the visible area of the camera around the node in each road network data based on the position information and the visible area information of the camera in the video surveillance network, so as to meet the coverage requirement corresponding to the node in each road network data.
5. The system for detecting the abnormal events based on the video monitoring high-risk areas as claimed in claim 4, wherein the video monitoring module comprises a plurality of sub video monitoring modules distributed on a map and correspondingly networked with the cloud server.
6. The system for detecting the abnormal events based on the video monitoring high-risk areas as claimed in claim 1, wherein the second determining module is further configured to perform early warning analysis on the abnormal events, and search for the correlation of the abnormal events in space and time; establishing an abnormal behavior database through the abnormal behavior alarm information received by the front-end video monitoring equipment; the abnormal behavior database provides metadata related to the event for subsequent analysis, then correlation analysis is carried out on the single-point historical data, and the time autocorrelation of the single-point historical data is analyzed; when a monitoring point detects that abnormal behaviors occur, correlation analysis with historical data in the database is started.
7. The system for detecting the abnormal events based on the video monitoring high-risk areas as claimed in claim 6, wherein the second determining module is further configured to classify the monitoring targets according to the grades of risk assessment, and set corresponding abnormal behaviors for each type of monitoring targets; and constructing a risk evaluation model according to the established abnormal behavior database, and endowing each type of behavior with a risk weight according to the potential safety voyage consequences of the abnormal behavior.
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