CN111309151B - Control method of school monitoring equipment - Google Patents
Control method of school monitoring equipment Download PDFInfo
- Publication number
- CN111309151B CN111309151B CN202010131335.2A CN202010131335A CN111309151B CN 111309151 B CN111309151 B CN 111309151B CN 202010131335 A CN202010131335 A CN 202010131335A CN 111309151 B CN111309151 B CN 111309151B
- Authority
- CN
- China
- Prior art keywords
- behavior
- support vector
- vector machine
- monitoring equipment
- monitoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000012706 support-vector machine Methods 0.000 claims abstract description 49
- 239000002245 particle Substances 0.000 claims abstract description 36
- 238000012360 testing method Methods 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims abstract description 12
- 230000006399 behavior Effects 0.000 claims description 77
- 229920000742 Cotton Polymers 0.000 claims description 18
- 238000012806 monitoring device Methods 0.000 claims description 14
- 238000004140 cleaning Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 9
- 230000003238 somatosensory effect Effects 0.000 abstract description 3
- 238000004891 communication Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- 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
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Human Computer Interaction (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Psychiatry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Economics (AREA)
- Evolutionary Biology (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Social Psychology (AREA)
- General Business, Economics & Management (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Alarm Systems (AREA)
- Image Processing (AREA)
Abstract
The invention belongs to the technical field of monitoring equipment control, and particularly discloses a control method of school monitoring equipment, which comprises the following steps: a monitoring equipment network is arranged on the campus, and a body sensing equipment and a brightness sensor are connected to the monitoring equipment. The monitoring equipment acquires a plurality of preset behavior images and establishes a behavior image instruction sample library. And establishing a support vector machine, and optimizing network parameters of the support vector machine by utilizing a particle swarm algorithm. And dividing the marked behavior image samples into a training set and a testing set, and then training the support vector machine. The motion sensing equipment collects a plurality of behavior actions and establishes an action instruction database. And calling a corresponding operation instruction to control the monitoring equipment according to the brightness of the brightness sensor and the preset behavior or the preset behavior image acquired by the somatosensory equipment or the support vector machine. When the maintenance personnel maintain the campus network, the campus monitoring network can be operated without contacting with an operator of a monitoring room, and therefore maintenance operation is completed.
Description
Technical Field
The invention belongs to the technical field of monitoring equipment control, and particularly relates to a control method for school monitoring equipment.
Background
In the campus, because of the school personnel are intensive, and have more teaching equipment, mr student's private article in the school, consequently can all take place more theft every year, can take place to fight etc. phenomenon simultaneously, consequently installed supervisory equipment in the campus, supervisory equipment logs in the environment video of school in real time, can retrieve above-mentioned action.
Install supervisory equipment in the campus and give the theft of campus, events such as fighting provide better video evidence, nevertheless the general area in campus is all very big, and the monitor room is far away again from the distance of every camera, when needs are examined the campus monitoring network and are maintained, if need temporarily close supervisory equipment, when operations such as opening supervisory equipment, then need the monitor room to cooperate, if maintenance personal carries out the signal of communication when relatively poor through intercom and monitoring personnel, then cooperation that can not be fine, can bring inconvenience for maintenance work.
Disclosure of Invention
The invention aims to provide a control method of school monitoring equipment, so as to overcome the defect that when a monitoring room is required to be matched for checking and maintaining a campus monitoring network, communication signals between maintenance personnel and monitoring personnel through a telephone are poor and the school monitoring equipment cannot be matched well.
In order to achieve the above object, the present invention provides a control method for a school monitoring device, including:
s1, arranging a monitoring equipment network on a campus, wherein the monitoring equipment is connected with a body sensing equipment and a brightness sensor;
s2, the monitoring equipment acquires a plurality of preset behavior images, each preset behavior corresponds to a specific operation instruction of the monitoring equipment, and behavior characteristics of the behavior images are marked to form a behavior image instruction sample library;
s3, establishing a support vector machine, and optimizing the network parameters of the support vector machine by utilizing a particle swarm algorithm to form the support vector machine with the optimal network parameters;
s4, dividing the marked behavior image sample into a training set and a testing set, inputting the training set into training behavior characteristic data of a support vector machine with optimal network parameters, and testing the trained support vector machine by using the testing set to obtain the support vector machine capable of predicting behaviors;
s5, the motion sensing device collects a plurality of behavior actions, each behavior action corresponds to a specific operation instruction of the monitoring device, and the behavior actions and the corresponding operation instructions are integrated to form an action instruction database;
s6, acquiring a brightness value in real time by a brightness sensor, closing support vector machine prediction when the brightness value is lower than a preset value, opening the motion sensing equipment, acquiring behavior actions in real time by the motion sensing equipment, and calling corresponding operation instructions according to the behavior actions to control the monitoring equipment;
and when the brightness value is higher than a preset value, the motion sensing equipment is closed, support vector machine prediction is performed, the monitoring equipment acquires behavior images in real time, the support vector machine capable of predicting behaviors is adopted to perform behavior prediction on continuous frame monitoring images, and if a preset behavior image is predicted, a corresponding operation instruction is called to control the monitoring equipment.
Preferably, in the above technical solution, step S2 specifically includes:
s201, setting parameters of the support vector machine: punishment parameter C, RBF nuclear parameter delta and loss function epsilon parameter; wherein, the range of the penalty parameter C is [1, 100], the range of the RBF nuclear parameter delta is [0.1, 100], and the range of the loss function epsilon parameter is [0.001, 1 ];
s202, initializing relevant parameters of particle swarm: setting population quantity, maximum iteration times, learning factors and inertia weight, and randomly giving an initial position and speed of each particle;
s203, determining a fitness evaluation function, and evaluating the fitness of each particle according to the fitness function;
s204, enabling the extreme value of the fitness of each particle to be in pbest, and enabling the fitness of all the optimal individuals to be in global extreme value gbest;
s205, updating the positions and the speeds of the particles according to the following formula (1) and formula (2), and setting pbest as a new position if the fitness of the particles is better than the pbest;
v k+1 ＝wv k +c 1 r 1 (pbest k -x k )+c 2 r 2 (gbest k -x k ) (1)
x k+1 ＝x k +v k+1 (2)
in the formula: v. of k And x k The velocity vector and position of the current particle; v. of k+1 And x k+1 Updating the velocity vector and the position of the particle; pbest k Represents the current optimal solution position, gbest, of the particle k Representing the optimal solution position of the whole population; w is the inertial weight, and w is 0.8; c. C 1 And c 2 Is a learning factor; r is 1 And r 2 A uniformly distributed random number between 0 and 1;
s206, checking whether the iteration times or the minimum error requirement is met, if so, stopping iteration, and storing the overall optimal position value of the particle swarm, otherwise, turning to S203 to continue calculation;
and S207, outputting the gbest to obtain parameters of the support vector machine so as to establish the optimal nonlinear support vector machine.
Preferably, in the above technical solution, the monitoring device includes: the shell, camera, reflector panel, light source, controller, clean cotton brush, first motor, timer, second motor and the cover body, the shell is hexahedron cylinder form, and every cylinder of the shell of hexahedron cylinder form all is equipped with a camera, the camera of camera extends every cylinder, and the top of every camera is equipped with the reflector panel, the reflector panel with be equipped with the light source between the camera, the one end of cleaning the cotton brush articulate in one side of camera, the one end of the cover body articulate in clean one side of cotton brush, first motor is used for driving the cover body cover in clean on the cotton brush, the second motor is used for driving and cleans the cotton brush toward the camera surface swing back and forth of camera, wherein, timer, camera, light source, first motor and second motor respectively with the controller is connected.
Preferably, in the foregoing technical solution, the light source of the monitoring device is not lit in an initial state, when the support vector machine predicts an image with a predetermined behavior, the light source is turned on, the monitoring device predicts an image acquired after the prediction again by using the support vector machine capable of predicting the predetermined behavior, at this time, if the image with the predetermined behavior is predicted again, the corresponding operation instruction is called, and if the image with the predetermined behavior is not predicted again, the corresponding operation instruction is not called.
Preferably, in the above technical scheme, the behavior includes lifting both hands, lifting the left hand and kicking the right foot, lifting the right hand and kicking the left foot.
Preferably, in the above technical solution, the plurality of predetermined behavior images include a two-hand-lifting image, a left-hand-lifting and right-foot-kicking image, and a right-hand-lifting and left-foot-kicking image.
Compared with the prior art, the invention has the following beneficial effects:
according to the control method of the school monitoring equipment, the somatosensory equipment is used for capturing the preset action or the support vector machine is used for capturing the image of the preset action and converting the image into the operation instruction for controlling the monitoring equipment, so that maintenance personnel can operate the campus monitoring network without contacting with an operator in a monitoring room when maintaining the campus network, and maintenance operation is completed.
Drawings
Fig. 1 is a flowchart of a control method of the school monitoring device of the present invention.
FIG. 2 is a flow chart of the PSO optimization algorithm of the present invention.
Fig. 3 is a top view of the monitoring device of the present invention.
Fig. 4 is a front view of the monitoring device of the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
As shown in fig. 1, the control method of the school monitoring device in this embodiment includes:
and S1, arranging a monitoring equipment network on the campus, wherein each monitoring equipment is connected with a body sensing equipment and a brightness sensor. Specifically, monitoring devices are installed in all corners of the campus, and the monitoring devices are connected to the server, so that a monitoring network is formed.
And S2, the monitoring equipment acquires a plurality of preset behavior images, each preset behavior corresponds to a specific operation instruction of the monitoring equipment, and the behavior characteristics of the plurality of behavior images are marked, namely, the characteristics are extracted to form a behavior image instruction sample library. The plurality of preset behavior images comprise a two-hand lifting image, a left-hand lifting image, a right-foot lifting image, a left-hand lifting image and the like, wherein the two-hand lifting image is a command for closing the monitoring equipment, the left-hand lifting image and the right-foot lifting image are commands for opening the monitoring equipment, and the right-hand lifting image and the left-foot lifting image are commands for closing the monitoring equipment in a trial run for 10 minutes.
And S3, establishing a support vector machine, and optimizing the network parameters of the support vector machine by utilizing a particle swarm algorithm to form the support vector machine with the optimal network parameters. And taking the input of the predetermined behavior image as a target parameter x, and the output as y (predetermined behavior characteristic), obviously taking y (f) (x) as a nonlinear relation, taking the predetermined behavior image x as an input sample of the particle swarm PSO-SVM support vector machine model, and outputting the predetermined behavior characteristic y after being processed by the particle swarm PSO-SVM support vector machine model.
And S4, dividing the marked behavior image samples into a training set and a test set, randomly extracting the first 90% of the sample data as the training set and the last 10% as the test set, inputting the training set into the training behavior characteristic data of the support vector machine with the optimal network parameters, and testing the trained support vector machine by using the test set to obtain the support vector machine capable of predicting behaviors.
S5, acquiring a plurality of behavior actions by the motion sensing device, enabling each behavior action to correspond to a specific operation instruction of the monitoring device, and integrating the behavior actions and the corresponding operation instructions to form an action instruction database; the action includes lifting both hands, lifting left hand and kicking right foot, lifting right hand and kicking left foot. If the two hands are lifted to close the monitoring equipment instruction, the left hand is lifted and the right foot is kicked to open the monitoring equipment instruction, and the right hand is lifted and the left foot is kicked to test for 10 minutes to close the instruction.
And S6, acquiring the brightness value in real time by the brightness sensor, closing the support vector machine for prediction when the brightness value is lower than a preset value, opening the motion sensing equipment, acquiring the behavior action in real time by the motion sensing equipment, and calling a corresponding operation instruction according to the behavior action to control the monitoring equipment.
And when the brightness value is higher than the preset value, the motion sensing equipment is closed, the support vector machine is used for predicting, the monitoring equipment acquires the behavior image in real time, the support vector machine capable of predicting the behavior is used for performing behavior prediction on the continuous frame monitoring image, and if the preset behavior image is predicted, the corresponding operation instruction is called to control the monitoring equipment.
As shown in fig. 2, step S2 specifically includes:
s201, setting parameters of the support vector machine: punishment parameter C, RBF nuclear parameter delta and loss function epsilon parameter; wherein, the range of the penalty parameter C is [1, 100], the range of the RBF nuclear parameter delta is [0.1, 100], and the range of the loss function epsilon parameter is [0.001, 1 ].
S202, initializing relevant parameters of the particle swarm: and setting the population quantity, the maximum iteration times, the learning factors and the inertia weight, and randomly endowing the initial position and the speed of each particle.
S203, determining a fitness evaluation function, and evaluating the fitness of each particle according to the fitness function.
And S204, enabling the extreme value of the fitness of each particle to exist in pbest, and enabling the fitness of all the optimal individuals to exist in a global extreme value gbest.
S205, updating the positions and the speeds of the particles according to the following formula (1) and formula (2), and setting pbest as a new position if the fitness of the particles is better than the pbest;
v k+1 ＝wv k +c 1 r 1 (pbest k -x k )+c 2 r 2 (gbest k -x k ) (1)
x k+1 ＝x k +v k+1 (2)
in the formula: v. of k And x k The velocity vector and position of the current particle; v. of k+1 And x k+1 Updating the velocity vector and the position of the particle; pbest k Represents the current optimal solution position, gbest, of the particle k Representing the optimal solution position of the whole population; w is the inertial weight, and w is 0.8; c. C 1 And c 2 Is a learning factor; r is 1 And r 2 Is a uniformly distributed random number between 0 and 1.
S206, checking whether the iteration times or the minimum error requirement is met, if so, stopping iteration, and storing the overall optimal position value of the particle swarm, otherwise, turning to S203 to continue calculation.
And S207, outputting the gbest to obtain parameters of the support vector machine so as to establish the optimal nonlinear support vector machine.
Further, as shown in fig. 3 to 4, the monitoring apparatus includes: the camera comprises a shell 1, a camera 2, a reflector 4, a light source 3, a controller 9, a cleaning cotton brush 7, a first motor 6, a second motor 8, a timer and a cover body 5, wherein the shell 1 is in a hexahedral cylinder shape, each cylindrical surface of the hexahedral cylinder-shaped shell 1 is provided with one camera 2, a camera of the camera 2 extends out of each cylindrical surface, the reflector 4 is arranged above each camera 2, the light source 3 is arranged between the reflector 4 and the camera 2, one end of the cleaning cotton brush 7 is hinged to one side of the camera 2, one end of the cover body 5 is hinged to one side of the cleaning cotton brush 7, the first motor 6 is used for driving the cover body 5 to cover the cleaning cotton brush 7, the second motor 8 is used for driving the cleaning cotton brush 7 to swing back and forth towards the surface of the camera 2, and the timer, the camera 2, the light source 3, the first motor 6 and the second motor 8 are respectively connected with the controller 9, the controller 9 is connected to the server of the monitoring room.
When the monitoring equipment works, the camera acquires the video in real time, transmits the video to the controller, and transmits the video to the monitoring center after being processed by the controller. Further, in the normality, the cover body covers on cleaning the cotton brush, timing through the timer, start once every 5 hours if regularly, when scheduled time, then start first motor and rotate and drive the cover body and upwards turn up, then the second motor drives and cleans the cotton brush toward the camera surface swing back and forth of camera, thereby clean the camera, be located the height all the year round with solving the camera, can't be by the mesh of cleaning, after the swing back and forth is several times, the second motor drives and cleans the cotton brush and resets, first motor drives the cover body and resets, thereby accomplish the camera and clean work.
Further, if the weather is rainy, in order to prevent misjudgment, the initial state of the light source of the monitoring equipment is not bright, when the support vector machine predicts a behavior image, the controller of the monitoring equipment controls to turn on the light source, the monitoring equipment predicts the acquired image again by using the support vector machine capable of predicting the preset behavior, at the moment, if the predicted behavior image is the preset behavior image again, a corresponding operation instruction is called, and if the predicted behavior image is not the preset behavior image, the corresponding operation instruction is not called. So that when a predetermined behavior is recognized, a predetermined behavior image with increased brightness can be acquired again using the apparatus and method, thereby improving the recognition rate of the predetermined behavior.
According to the control method of the school monitoring equipment, the somatosensory equipment is used for capturing the preset action or capturing the image of the preset action and converting the image into the operation instruction for controlling the monitoring equipment, so that when a maintenance worker maintains the campus network, the campus monitoring network can be operated without contacting with an operator in a monitoring room, and therefore maintenance operation is completed.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (2)
1. A control method of school monitoring equipment is characterized by comprising the following steps:
s1, arranging a monitoring equipment network on a campus, wherein the monitoring equipment is connected with a body sensing equipment and a brightness sensor;
s2, the monitoring equipment acquires a plurality of preset behavior images, each preset behavior corresponds to a specific operation instruction of the monitoring equipment, and behavior characteristics of the behavior images are marked to form a behavior image instruction sample library;
s3, establishing a support vector machine, and optimizing the network parameters of the support vector machine by utilizing a particle swarm algorithm to form the support vector machine with the optimal network parameters;
s4, dividing the marked behavior image sample into a training set and a testing set, inputting the training set into training behavior characteristic data of a support vector machine with optimal network parameters, and testing the trained support vector machine by using the testing set to obtain the support vector machine capable of predicting behaviors;
s5, acquiring a plurality of behavior actions by the motion sensing device, enabling each behavior action to correspond to a specific operation instruction of the monitoring device, and integrating the behavior actions and the corresponding operation instructions to form an action instruction database;
s6, acquiring a brightness value in real time by a brightness sensor, closing support vector machine prediction when the brightness value is lower than a preset value, opening the motion sensing equipment, acquiring behavior actions in real time by the motion sensing equipment, and calling corresponding operation instructions according to the behavior actions to control the monitoring equipment;
when the brightness value is higher than a preset value, the motion sensing equipment is closed, support vector machine prediction is performed, behavior images are obtained in real time by the monitoring equipment, the support vector machine capable of predicting behaviors is adopted to perform behavior prediction on continuous frame monitoring images, and if a preset behavior image is predicted, a corresponding operation instruction is called to control the monitoring equipment;
the monitoring device includes: the camera comprises a shell, cameras, a reflector, a light source, a controller, a timer, a cleaning cotton brush, a first motor, a second motor and a cover body, wherein the shell is in a hexahedral cylinder shape, each cylindrical surface of the hexahedral cylinder shaped shell is provided with one camera, each cylindrical surface extends out of each camera head of the camera, the reflector is arranged above each camera, the light source is arranged between the reflector and the camera, one end of the cleaning cotton brush is hinged to one side of the camera, one end of the cover body is hinged to one side of the cleaning cotton brush, the first motor is used for driving the cover body to cover the cleaning cotton brush, the second motor is used for driving the cleaning cotton brush to swing back and forth towards the surface of the camera head of the camera, and the timer, the cameras, the light source, the first motor and the second motor are respectively connected with the controller;
the light source of the monitoring equipment is not bright in an initial state, when the support vector machine predicts a preset behavior image, the light source is turned on, the monitoring equipment predicts an image acquired after the support vector machine capable of predicting the preset behavior predicts the image again, at the moment, if the image is predicted to be the preset behavior image again, a corresponding operation instruction is called, and if the image is not predicted to be the preset behavior image, the corresponding operation instruction is not called;
the behavior actions comprise lifting two hands, lifting the left hand and kicking the right foot, lifting the right hand and kicking the left foot, and the plurality of preset behavior images comprise lifting two-hand images, lifting the left hand and kicking the right foot images, and lifting the right hand and kicking the left foot images.
2. The method for controlling school monitoring equipment according to claim 1, wherein step S2 specifically includes:
s201, setting parameters of the support vector machine: punishment parameter C, RBF nuclear parameter delta and loss function epsilon parameter; wherein, the range of the penalty parameter C is [1, 100], the range of the RBF nuclear parameter delta is [0.1, 100], and the range of the loss function epsilon parameter is [0.001, 1 ];
s202, initializing relevant parameters of particle swarm: setting population quantity, maximum iteration times, learning factors and inertia weight, and randomly endowing the initial position and speed of each particle;
s203, determining a fitness evaluation function, and evaluating the fitness of each particle according to the fitness function;
s204, enabling the extreme value of the fitness of each particle to be in pbest, and enabling the fitness of all the optimal individuals to be in global extreme value gbest;
s205, updating the positions and the speeds of the particles according to the following formulas (1) and (2), and setting pbest as a new position if the fitness of the particles is better than the pbest;
v k+1 ＝wv k +c 1 r 1 (pbest k -x k )+c 2 r 2 (gbest k -x k ) (1)
x k+1 ＝x k +v k+1 (2)
in the formula: v. of k And x k The velocity vector and position of the current particle; v. of k+1 And x k+1 Updating the velocity vector and the position of the particle; pbest k Represents the current optimal solution position, gbest, of the particle k Representing the optimal solution position of the whole population; w is the inertial weight, and w is 0.8; c. C 1 And c 2 Is a learning factor; r is 1 And r 2 A uniformly distributed random number between 0 and 1;
s206, checking whether the iteration times or the minimum error requirement is met, if so, stopping iteration, and storing the overall optimal position value of the particle swarm, otherwise, turning to S203 to continue calculation;
and S207, outputting the gbest to obtain parameters of the support vector machine so as to establish the optimal nonlinear support vector machine.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010131335.2A CN111309151B (en) | 2020-02-28 | 2020-02-28 | Control method of school monitoring equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010131335.2A CN111309151B (en) | 2020-02-28 | 2020-02-28 | Control method of school monitoring equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111309151A CN111309151A (en) | 2020-06-19 |
| CN111309151B true CN111309151B (en) | 2022-09-16 |
Family
ID=71159535
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010131335.2A Active CN111309151B (en) | 2020-02-28 | 2020-02-28 | Control method of school monitoring equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111309151B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009166323A (en) * | 2008-01-15 | 2009-07-30 | Sigumakkusu Kk | Injection molding machine monitor |
| CN104915003A (en) * | 2015-05-29 | 2015-09-16 | 深圳奥比中光科技有限公司 | Somatosensory control parameter adjusting method, somatosensory interaction system and electronic equipment |
| CN105095866A (en) * | 2015-07-17 | 2015-11-25 | 重庆邮电大学 | Rapid behavior identification method and system |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BR0303336A (en) * | 2002-07-02 | 2004-04-27 | Matsushita Electric Ind Co Ltd | Motion vector derivation method, animated image encoding method, and animated image decoding method |
| CN102161202B (en) * | 2010-12-31 | 2012-11-14 | 中国科学院深圳先进技术研究院 | Full-view monitoring robot system and monitoring robot |
| TWI456430B (en) * | 2012-12-07 | 2014-10-11 | Pixart Imaging Inc | Gesture recognition apparatus, operating method thereof, and gesture recognition method |
| CN107037878A (en) * | 2016-12-14 | 2017-08-11 | 中国科学院沈阳自动化研究所 | A kind of man-machine interaction method based on gesture |
| CN110069650B (en) * | 2017-10-10 | 2024-02-09 | 阿里巴巴集团控股有限公司 | Searching method and processing equipment |
| CN109640032B (en) * | 2018-04-13 | 2021-07-13 | 河北德冠隆电子科技有限公司 | Five-dimensional early warning system based on artificial intelligence multi-element panoramic monitoring detection |
| CN108830304A (en) * | 2018-05-29 | 2018-11-16 | 深圳明创自控技术有限公司 | A kind of image identification system based on support vector machines |
| GB2575282A (en) * | 2018-07-04 | 2020-01-08 | Arm Ip Ltd | Event entity monitoring network and method |
| CN109919202A (en) * | 2019-02-18 | 2019-06-21 | 新华三技术有限公司合肥分公司 | Disaggregated model training method and device |
-
2020
- 2020-02-28 CN CN202010131335.2A patent/CN111309151B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009166323A (en) * | 2008-01-15 | 2009-07-30 | Sigumakkusu Kk | Injection molding machine monitor |
| CN104915003A (en) * | 2015-05-29 | 2015-09-16 | 深圳奥比中光科技有限公司 | Somatosensory control parameter adjusting method, somatosensory interaction system and electronic equipment |
| CN105095866A (en) * | 2015-07-17 | 2015-11-25 | 重庆邮电大学 | Rapid behavior identification method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111309151A (en) | 2020-06-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10740676B2 (en) | Passive pruning of filters in a convolutional neural network | |
| CN109870987A (en) | Platform of internet of things management method and its system suitable for large-sized workshop site operation | |
| CN112200011B (en) | Aeration tank state detection method, system, electronic equipment and storage medium | |
| CN112731981B (en) | Automatic remote control system of water conservancy gate | |
| CN112219814B (en) | Tobacco beetle situation monitoring and early warning system | |
| CN111309151B (en) | Control method of school monitoring equipment | |
| WO2020039559A1 (en) | Information processing device, information processing method, and work evaluation system | |
| CN112219815B (en) | Tobacco beetle condition monitoring and early warning method | |
| CN115951617A (en) | Control system based on digital twins | |
| CN117235443A (en) | Electric power operation safety monitoring method and system based on edge AI | |
| CN117895899B (en) | Photovoltaic panel cleanliness detection method and system | |
| CN117523617B (en) | Insect pest detection method and system based on machine learning | |
| CN114940424A (en) | Elevator detection method, system, computer equipment and readable medium | |
| CN116698977B (en) | Coal gangue recognition sensor and recognition method based on sound signals | |
| CN113438469A (en) | Automatic testing method and system for security camera | |
| CN110045133A (en) | A kind of consumptive material monitoring device and monitoring method | |
| CN111923042B (en) | Virtualization processing method and system for cabinet grid and inspection robot | |
| CN113138894B (en) | Experimental equipment monitoring method based on power parameter monitoring and screen information identification | |
| CN109934212A (en) | The method of imaging device and automatic identification image based on wide-angle | |
| CN112861681B (en) | Pipe gallery video intelligent analysis method and system based on cloud processing | |
| CN113703341A (en) | Simulation test system for driver monitoring | |
| CN1438600A (en) | Monitoring analysis system for animal sport, behaviour and electrophysiological quota in free activity experiment | |
| CN208766849U (en) | A kind of Machine Tools Electric practice training examination device | |
| CN107144962B (en) | A kind of system and method for finding the optimal running parameter of imaging system | |
| García et al. | Mixed reality educational environment for robotics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20200619 Assignee: Guangxi Jiulong Electronic Technology Co.,Ltd. Assignor: GUILIN University OF ELECTRONIC TECHNOLOGY Contract record no.: X2023980045660 Denomination of invention: A Control Method for School Monitoring Equipment Granted publication date: 20220916 License type: Common License Record date: 20231105 |