CN111309151A - Control method of school monitoring equipment - Google Patents
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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, 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;
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;
vk+1＝wvk+c1r1(pbestk-xk)+c2r2(gbestk-xk) (1)
xk+1＝xk+vk+1(2)
in the formula: v. ofkAnd xkThe velocity vector and position of the current particle; v. ofk+1And xk+1Updating the velocity vector and the position of the particle; pbestkRepresents the current optimal solution position, gbest, of the particlekRepresenting the optimal solution position of the whole population; w is the inertial weight, and w is 0.8; c. C1And c2Is a learning factor; r is1And r2A 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 testing set, randomly extracting the first 90% of sample data as the training set and the last 10% as the testing set, inputting the training set into a support vector machine with the optimal network parameters to train behavior characteristic data, 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; 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 particle swarm: and setting the population quantity, the maximum iteration times, the learning factor and the inertia weight, and randomly giving 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;
vk+1＝wvk+c1r1(pbestk-xk)+c2r2(gbestk-xk) (1)
xk+1＝xk+vk+1(2)
in the formula: v. ofkAnd xkThe velocity vector and position of the current particle; v. ofk+1And xk+1Updating the velocity vector and the position of the particle; pbestkRepresents the current optimal solution position, gbest, of the particlekRepresenting the optimal solution position of the whole population; w is the inertial weight, and w is 0.8; c. C1And c2Is a learning factor; r is1And r2Is 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, 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 the light source to be turned on, 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 image is the preset behavior image, the corresponding operation instruction is called, and if the predicted 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 (6)
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;
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.
2. The control method of the anti-theft monitoring device for the basement according to claim 1, wherein the 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;
vk+1＝wvk+c1r1(pbestk-xk)+c2r2(gbestk-xk) (1)
xk+1＝xk+vk+1(2)
in the formula: v. ofkAnd xkThe velocity vector and position of the current particle; v. ofk+1And xk+1Updating the velocity vector and the position of the particle; pbestkRepresents the current optimal solution position, gbest, of the particlekRepresenting the optimal solution position of the whole population; w is the inertial weight, and w is 0.8; c. C1And c2Is a learning factor; r is1And r2A 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.
3. The control method of the anti-theft monitoring device for the basement according to claim 1, wherein the monitoring device comprises: the shell, camera, reflector panel, light source, controller, timer, clean cotton brush, first motor, 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 that cleans 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 clean 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.
4. The control method of the anti-theft monitoring device for the basement according to claim 3, wherein the light source of the monitoring device is not lighted in the initial state, when the support vector machine predicts the image of the predetermined behavior, the light source is turned on, the monitoring device predicts the image collected after the predetermined behavior is predicted again by using the support vector machine capable of predicting the predetermined behavior, at this time, if the image of the predetermined behavior is predicted again, the corresponding operation instruction is called, and if the image of the predetermined behavior is not predicted again, the corresponding operation instruction is not called.
5. The control method of the anti-theft monitoring equipment for the basement according to claim 1, wherein the action includes lifting two hands, lifting left hand and kicking right foot, lifting right hand and kicking left foot.
6. The control method of the anti-theft monitoring device for the basement according to claim 1, wherein the plurality of predetermined behavior images comprise a two-hand-lifting image, a left-hand-lifting and right-foot-kicking image, a right-hand-lifting and left-foot-kicking image.
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