CN110009539A - A kind of student is in school learning state smart profile system and application method - Google Patents

A kind of student is in school learning state smart profile system and application method Download PDF

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CN110009539A
CN110009539A CN201910293925.2A CN201910293925A CN110009539A CN 110009539 A CN110009539 A CN 110009539A CN 201910293925 A CN201910293925 A CN 201910293925A CN 110009539 A CN110009539 A CN 110009539A
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student
behavior
learning state
image
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孙学诗
杜超
刘玉松
王和真
王洋
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Yantai Engineering and Technology College
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Abstract

The present invention relates to a kind of students in school learning state smart profile system and application method, what is solved is the high technical problem of computation complexity of behavioural analysis, by using include image data acquiring module M1, recognition of face locating module M2, using the behavioural analysis module M3 of extensive control methods, learning state analysis module M6, filing processing module M7;The student further includes scene behavior library module M10, archive of student module M8 in school learning state smart profile system, and the archive of student module M8, which is also connected with, is provided with data aggregation module M9;The filing processing module M7 pushes to archive of student module M8 after handling the information package of learning state analysis module M6;Technical solution, the problem is preferably resolved, in wisdom education informations.

Description

A kind of student is in school learning state smart profile system and application method
Technical field
The present invention relates to wisdom education informations and image intelligent process field, and in particular to a kind of student is in school learning state Smart profile system and application method.
Background technique
In wisdom teaching field, student is one of the key element paid close attention in school learning state.The prior art In, limited several action recognitions can be realized by image recognition technology in the research of school learning state to student, such as: it sees Book writes, looks at the blackboard.Support is provided for the research that automatically analyzes of learning state to a certain extent, but there is a variety of Nonrecognition is acted, and lacks the systematic Study to student's learning state general character and individual character, analytical error is excessive, causes existing Technological applicability is not high.
Existing student school learning state smart profile system and application method cannot take into account school general character and Otherness, analysis efficiency is low, and identification error is big.The present invention provides a kind of student in school learning state smart profile system and use Method solves above-mentioned technical problem.
Summary of the invention
The technical problem to be solved by the present invention is to the high skills of the computation complexity of behavioural analysis existing in the prior art Art problem.The new student of one kind is provided in school learning state smart profile system, the student is in school learning state smart profile system System has the characteristics that the computation complexity of reduction behavioural analysis, suitable for the real-time analysis to student group learning state.
In order to solve the above technical problems, the technical solution adopted is as follows:
In school learning state smart profile system, the student includes in school learning state smart profile system by a kind of student Image data acquiring module M1, recognition of face locating module M2, the behavioural analysis module M3 using extensive control methods, study shape State analysis module M6, filing processing module M7;
The student further includes scene behavior library module M10, archive of student module in school learning state smart profile system M8, the archive of student module M8, which is also connected with, is provided with data aggregation module M9;
The filing processing module M7 pushes to archive of student after handling the information package of learning state analysis module M6 Module M8;
The data aggregation module M9 is docked with teaching education administration system, realizes the convergence and application of data;
The scene behavior library module M10 includes Activity recognition basic data collection.
The working principle of the invention: the general character and otherness of present invention school in order to balance improves analysis efficiency, drop Low identification error, the present invention are also added into extensive control methods, greatly reduce the computation complexity of behavioural analysis, especially suitable for Real-time analysis to student group learning state realizes and student intelligently files in school learning state, and individual students peel off The intelligence discovery of habit state and reason, and it is used for scientific guidance teaching, it improves the quality of teaching.On the one hand row is greatly reduced Outlier Analysis for again the computation complexity of analysis, the behavior that on the other hand can attend class for student provides direct foundation.
In above scheme, for optimization, further, the behavioural analysis module M3 is connected with difficult sample using module M4, difficult sample are connected with abnormal scene processing module M5 using module;The scene behavior library module M10 further includes anomalous field Scape processing module M5 incoming new scene behavioral data collection.
The present invention also provides a kind of students in school learning state smart profile application method, and the student is in school learning state Smart profile application method is based on aforementioned student in school learning state smart profile system, and the application method includes:
Step 1, image data acquiring module M1 carry out image data acquiring, the face figure including acquiring student and teacher As information, as the crucial recognition property of archives, and the real-time image information of acquisition student and teacher, as state analysis Input information;
Step 2, recognition of face locating module M2 receive the reality of image data acquiring module M1 incoming student and teacher When image information, the face information in image is identified and is positioned;
Step 3, behavioural analysis module M3 are received from the recognition of face locating module M2 real-time image information transmitted and people After member's information, according to scene behavior library module M10, behavioural analysis is done using extensive control methods;
Step 4, peel off behavior screening, the behavior type that peels off evaluation, teacher of learning state analysis module M6 attend class row The team learning state at certain moment is generated for behavioral reasons analysis of analyzing, peel off;
The information of learning state analysis module M6 is packaged and is handled by step 5, and is pushed to archive of student module M8, archive of student module M8 are stored with personal information, total marks of the examination, history learning status information;
Step 6, the external interaction data of data aggregation module M9.
Further, step 2 includes:
S201 carries out recognition of face and obtains the feature of facial image, know to the facial image register information of student and teacher Another characteristic result is denoted as Rc, and it is stored in archive of student module M8, it is registered for personnel's archives;
S202 carries out recognition of face and positioning obtains image view to the real-time image information of collected student and teacher All people's face information is identified and is positioned in open country, is identified and is denoted as R with the characteristic results of positioninglcAfter obtaining characteristic results, people Face identifies that locating module M2 calls the face register information in archive of student module M8, and comparison obtains all people person in the visual field and believes Breath;
S203, recognition of face locating module M2 are defeated by the real-time image information and corresponding personal information of student and teacher Behavioural analysis module M3 is arrived out.
Further, step 3 includes:
Step S301, member's fast grouping, behavioural analysis module M3 read realtime graphic from archive of student module M8 first The student's school grade recognized in picture, if there is academic record, by marks sequencing in archive of student module M8 Gradient is divided into n group;If there is no academic record in archive of student module M8, by member definitions all in video image For a group;
Step 3012, image is decomposed by member, as unit of the group result in step 301, using recognition of face and is determined Position method identifies the member of each group from image, by rapid edge-detection method, image is pressed member's Region Decomposition, is generated Decomposition image for behavioural analysis;
Step S302 constructs depth convolutional neural networks behavior analyzer, carries out typical behaviour analysis to s class.
Further, include: by rapid edge-detection method
Step 30121: the member region navigated to using recognition of face carries out rapid edge-detection as target area;
Step 30122: circulation carries out above-mentioned detection, and defining cycle-index is the number of members in image, and decomposition is independent Decompose image, the input for behavior classifier;
Step 3013: the decomposition image in step 3012 being input to convolutional neural networks behavior classifier and is carried out by behavior Classification, is divided into s class.
Further, the convolutional neural networks behavior classifier building the following steps are included:
Step 30131: the data set of building convolutional neural networks behavior classifier is made with all big Activity recognition data sets For basic data set, the user's scene behavioral data collection generated by abnormal scene processing module M5 is as growth data collection, base Plinth data set and growth data collection collectively constitute the data set of convolutional neural networks behavior classifier, the convolutional neural networks row It is divided into training set, test set and verifying collection for the data set of classifier;
Step 30132: building is suitable for the convolutional neural networks of behavior classification, and defining the number of members in one group is m, member Behavior be divided into s class, s≤m selects a behavior as typical at random from s class, does behavioural analysis.
Further, include: in step S302
Step 3021, the sample data set of depth convolutional neural networks behavior analyzer is established, with big Activity recognition data Collection is as basic data set, and the user's scene behavioral data collection generated by abnormal scene processing module M5 is as growth data Collection, basic data collection and growth data collection collectively constitute the sample data set of depth convolutional neural networks behavior analyzer, described Sample data set is divided into training set, test set and verifying collection;Depth convolutional neural networks are trained and are tested, until To the depth convolutional neural networks behavior analyzer for meeting pre-defined threshold requirement;
Step 3022: the image after decomposition being input in depth convolutional neural networks behavior analyzer, behavior point is obtained Analysis is as a result, behavioural analysis result includes that goal behavior particularly belongs to which kind of behavior and goal behavior can not parse.
Step 3023: for behavioural analysis the result is that the goal behavior that can not be parsed, by corresponding goal behavior original graph Piece is input to difficult specimen sample module M4.
Further, the goal behavior that the difficulty specimen sample module M4 can not parse behavioural analysis module M3, In its time domain, preceding n1 seconds to latter n1 seconds of successive frame picture is intercepted, successive frame picture is then pushed to abnormal scene process mould Block M5;
Successive frame image information is pushed to user using event triggering method by the exception scene processing module M5, by User carries out artificial cognition, after typing differentiates result, by successive frame picture and differentiates that result is deposited into scene behavior library module M10 In, convolutional neural networks behavior classifier and depth to spread foundation Activity recognition database, in behavioural analysis module M3 The update iteration of convolutional neural networks behavior analyzer network model.
Beneficial effects of the present invention: the general character and otherness of present invention school in order to balance improves analysis efficiency, drop Low identification error, the present invention are also added into extensive control methods, greatly reduce the computation complexity of behavioural analysis, especially suitable for Real-time analysis to student group learning state realizes and student intelligently files in school learning state, and individual students peel off The intelligence discovery of habit state and reason, and it is used for scientific guidance teaching, it improves the quality of teaching.It joined abnormal scene process Module, solve the problems, such as prior art model can not in application scenarios the movement of automatic Iterative strange scene identification.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1, student is in school learning state smart profile system and method block diagram.
Fig. 2, group behavior analysis flow chart diagram.
Fig. 3, by member's exploded view as flow chart.
Fig. 4, learning state analysis flow chart diagram.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, is not used to limit The fixed present invention.
Embodiment 1
The present embodiment provides a kind of students in school learning state smart profile system, and such as Fig. 1, the student is in school study shape State smart profile system includes image data acquiring module M1, recognition of face locating module M2, using the row of extensive control methods For analysis module M3, learning state analysis module M6, filing processing module M7;
The student further includes scene behavior library module M10, archive of student module in school learning state smart profile system M8, the archive of student module M8, which is also connected with, is provided with data aggregation module M9;
The filing processing module M7 pushes to archive of student after handling the information package of learning state analysis module M6 Module M8;
The data aggregation module M9 is docked with teaching education administration system, realizes the convergence and application of data;
The scene behavior library module M10 includes Activity recognition basic data collection.
The general character and otherness of the present embodiment school in order to balance improves analysis efficiency, reduces identification error, this hair It is bright to be also added into extensive control methods, greatly reduce the computation complexity of behavioural analysis, especially suitable for learning to student group The real-time analysis of state realizes and student intelligently files in school learning state, and individual students peel off learning state and reason Intelligence discovery, and it is used for scientific guidance teaching, it improves the quality of teaching.On the one hand the calculating for greatly reducing behavioural analysis is multiple The Outlier Analysis of miscellaneous degree, the behavior that on the other hand can attend class again for student provides direct foundation.
Specifically, the behavioural analysis module M3 is connected with difficult sample using module M4, and difficult sample is connected using module It is connected to abnormal scene processing module M5;The scene behavior library module M10 further includes incoming new of abnormal scene processing module M5 Scene behavioral data collection.It joined abnormal scene processing module, solving prior art model can not be automatic in application scenarios The identification problem of the strange scene movement of iteration.
Wherein, the human face image information of student and teacher are acquired, mode can be to be acquired by camera, is also possible to Pass through management software uploading pictures.
The present embodiment also provides a kind of student in school learning state smart profile application method, and the application method includes:
Step 1, image data acquiring module M1 carry out image data acquiring, the face figure including acquiring student and teacher As information, as the crucial recognition property of archives, and the real-time image information of acquisition student and teacher, as state analysis Input information;
Step 2, recognition of face locating module M2 receive the reality of image data acquiring module M1 incoming student and teacher When image information, the face information in image is identified and is positioned;
Step 3, behavioural analysis module M3 are received from the recognition of face locating module M2 real-time image information transmitted and people After member's information, according to scene behavior library module M10, behavioural analysis is done using extensive control methods;
Step 4, peel off behavior screening, the behavior type that peels off evaluation, teacher of learning state analysis module M6 attend class row The team learning state at certain moment is generated for behavioral reasons analysis of analyzing, peel off;
The information of learning state analysis module M6 is packaged and is handled by step 5, and is pushed to archive of student module M8, archive of student module M8 are stored with personal information, total marks of the examination, history learning status information;
Step 6, the external interaction data of data aggregation module M9.
The real-time image information of the collected human face image information of image data acquiring module M1 and student and teacher will It is input to recognition of face locating module M2.
Recognition of face locating module M2, the module receive the reality of student and teacher that image data acquiring module M1 is transmitted When image information, the face information in image is identified and is positioned first, for the specific of recognition of face herein and positioning Step, the present embodiment, which is not done, specifically to be repeated, and those skilled in the art can refer to the prior art.
Specifically, the treatment process that the face information in image is identified and positioned includes:
S201. it to the facial image register information of student and teacher, carries out recognition of face and obtains the feature of facial image, know Another characteristic result is denoted as Rc, which is stored in archive of student module M8, for the registration of personnel's archives;
S202. it to the real-time image information of collected student and teacher, carries out recognition of face and positioning obtains image view All people's face information is identified and is positioned in open country, is identified and is denoted as R with the characteristic results of positioninglcAfter obtaining characteristic results, people Face identification locating module M2 can also call the face register information in archive of student module M8, and comparison obtains all people in the visual field Member's information;
S203, after S202 step, recognition of face locating module M2 is by the real-time image information of student and teacher and right The personal information answered is output to behavioural analysis module M3;
Behavioural analysis module M3 is received from the recognition of face locating module M2 real-time image information transmitted and personal information Afterwards, behavioural analysis is done.
In order to solve the problems, such as the recognition efficiency and ratio of defects of crowd behaviour analysis, analysis rate is improved, identification is reduced and misses Difference, the present embodiment use extensive control methods.
As shown in Fig. 2, as follows to the processing step of crowd behaviour analysis:
S3011, member's fast grouping:
Step 3011A: behavioural analysis module M3 has identified from archive of student module M8 reading realtime graphic picture first The student's school grade arrived is divided into n group by achievement gradient, is denoted as group respectively if having academic record in system 1 ... group n, in the present embodiment, group technology are group 1 by before marks sequencing 5%, and remaining preceding 10% is group 2, remaining For group 3.
Step 3011B: if there is no academic record in system, members all in video image are considered as one big Group.
Step 3012: referring to Fig. 3, image is as follows by the process that member decomposes:
As unit of grouping in step 301, identified from image first with existing recognition of face and location technology The target rapid edge-detection that is identified as of the member of each group out, each member provide foundation, will by rapid edge-detection method Image presses member's Region Decomposition, generates the decomposition image for being used for behavioural analysis, steps are as follows:
Step 30121: the member region navigated to using recognition of face carries out rapid edge-detection as target area.
Canny edge detection method is used in the present embodiment, is handled as follows:
(1) along the row and column of image coordinate, smoothing denoising filter is constructed respectively.
Based on common 2-d gaussian filters device, function representation are as follows:
The gradient of above-mentioned function is2-d gaussian filters device is divided into the row along image coordinate With two smoothing denoising filters of column.
(2) digital picture calculates direction and the amplitude of image gradient using the differential representation of adjacent domains single order local derviation.
For image M (x, y), (x, y) indicates image point pixel value, it is asked to obtain the local derviation of x and y respectively:
Therefore the amplitude of gradient are as follows:The direction of gradient are as follows:
(3) anti-local optimum processing is carried out to image, to obtain more accurate fringe region.
Two film size values in the amplitude and gradient direction are made ratio by the amplitude for successively calculating every bit pixel in two dimensional image Compared with, using the point of both greater than two film size values as marginal point, reject only one point for being greater than two film size values, in this way can anti-part most Advantage interferes edge, finally obtains more accurate fringe region.
Step 30122: circulation carries out above-mentioned detection, and cycle-index is the number of members in image.It can obtain all members Fringe region, decomposition be independent exploded view picture, with the input for behavior classifier.
Step 3013: the decomposition image in step 3012 being input to convolutional neural networks behavior classifier and is carried out by behavior Classification.
Wherein, convolutional neural networks behavior classifier building the following steps are included:
Step 30131: the data set of building convolutional neural networks behavior classifier, first with major Activity recognition data set As basic data set including HMDB51, UCF101 etc., and during the routine use of system, pass through abnormal scene process mould For user's scene behavioral data collection that block M5 is generated as growth data collection, the two collectively constitutes convolutional neural networks behavior classifier Data set, the data set be divided into training set, test set and verifying collection;
Step 30132: building is suitable for the convolutional neural networks of behavior classification.
In the present embodiment, the building of convolutional neural networks is not specifically limited, those skilled in the art can refer to existing Technology is trained and tests, directly using suitable convolutional neural networks, such as ResNet32 network, LeNet-5 network etc. To the convolutional neural networks behavior classifier for obtaining meeting pre-defined threshold requirement.
Assuming that the number of members in group 1 is m, the behavior of member is divided into s class, and s≤m selects a behavior at random from s class and makees Behavioural analysis is done for typical case, the behavioural analysis comprising m group member is simplified to analyze its s class behavior.On the one hand significantly Reduce the computation complexity of behavioural analysis, the Outlier Analysis for the behavior that on the other hand can attend class again for student provide directly according to According to.
S302. typical behaviour is analyzed: for the s class behavior in step 30132, carrying out typical behaviour analysis, processing step It is rapid as follows:
Step 3021: building depth convolutional neural networks behavior analyzer, the present embodiment is not to depth spacing neural network Building be specifically limited, those skilled in the art can refer to the prior art, such as ResNet32 network;
Step 3022: the sample data set of depth convolutional neural networks behavior analyzer is established, first with major behavior knowledge Other data set includes HMDB51, UCF101 etc. as basic data set, and during the routine use of system, passes through anomalous field For user's scene behavioral data collection that scape processing module M5 is generated as growth data collection, the two collectively constitutes depth convolutional Neural net The sample data set of network behavior analyzer, the data set are divided into training set, test set and verifying collection;To depth convolutional Neural Network is trained and tests, until the depth convolutional neural networks behavior analyzer for obtaining meeting pre-defined threshold requirement;
Step 3023: referring to described in step 3012, the image after decomposition being input to depth convolutional neural networks behavior point In parser, behavioural analysis result is obtained.It as a result include two kinds, as a result 1: which kind of behavior goal behavior particularly belongs to;As a result 2: mesh Mark behavior can not parse;
Step 3024: for the goal behavior that can not be parsed, behavior original image being input to difficult specimen sample mould In block M4.
Difficult specimen sample module M4, for the goal behavior that behavioural analysis module M3 can not be parsed, system is in its time domain It is interior, it intercepts first n1 seconds and arrives rear n1 seconds of dependent picture, the present embodiment uses first 3 seconds to latter 3 seconds successive frame pictures, then Continuous pictures are pushed to abnormal scene processing module M5.
Abnormal scene processing module M5, abnormal scene processing module M5 after receiving the successive frame picture that can not be parsed, Using event triggering method, image information is pushed to user interface, by manually to the successive frame picture that can not be parsed into Row differentiates, and after input result, difficult scenic picture and result are deposited into scene behavior library module M10 by system, to expand Basic Activity recognition database is opened up, to realize the convolutional neural networks behavior classifier and depth convolution in behavioural analysis module M3 The update iteration of neural network behavior analyzer network model.
Learning state analysis module M6, the processing step of such as Fig. 4, learning state analysis module include the following:
S601 peel off behavior screening: through behavioural analysis module M3 S302 step typical behaviour analysis after, group can be obtained Behavioural analysis result.The analysis result is presented for data, in the present embodiment, is stated with scatter plot.
Peel off the screening technique of behavior are as follows:
It (1), will when behavior analysis module M3 classifies to group behavior using convolutional neural networks behavior classifier Extract the two class behaviors individual of minimum number.
(2) after depth convolutional neural networks behavior analyzer does behavioural analysis to all kinds of typical behaviours, then according to result Obtain peeling off the concrete behavior of classification.
S602 peels off behavior type evaluation: the behavior of attending class is divided into three classes: it is front, neutral, negative, and to scene behavior library mould Behavior in block M10 is identified, to be used to evaluate the behavior of attending class of student.Such as: pay attention to the class-front, talk-neutrality, sleep- Negatively.
It is evaluated using the behavior type that peels off in the scene behavior library module, the classification concrete behavior that peels off to analyzing Carry out Types Assessment.
S603, teacher attend class behavioural analysis: will teacher real time video image sample after be input to behavioural analysis module M3, Teacher is obtained to attend class behavioural analysis result.The behavior of teacher includes: writing on the blackboard, teaching, reads, says multimedia etc., is learnt in student In the research of state analysis, the behavior of attending class of teacher is also important references.
In the present embodiment, teacher only is being recognized when attend class behavior, the sleep behavior of student is just be evaluated as It negatively, is otherwise neutrality.
The behavioral reasons that peel off analysis: S604 attends class behavior and the behavior the selection result that peels off with reference to teacher, can determine student Peel off behavior the reason of.Such as: teacher says class hour, certain student sleep.
S605, generates the team learning state at certain moment: student's status information include: along time locus behavior record, Peel off behavior record etc..
The information of learning state analysis module M6 is packaged and is handled by filing processing module M7, and is pushed to student Profile module M8.
The personal information of student, total marks of the examination, history learning status information etc. are stored in student by archive of student module M8 In profile module M8.
Data aggregation module M9 is docked with teaching education administration system by modes such as data flows, is realized the convergences of data and is answered With.
Scene behavior library module M10, each Activity recognition basic data collection is stored in M10 module, in conjunction with anomalous field The new scene behavioral data collection that scape processing module M5 is transmitted has collectively constituted the scene behavior library suitable for this scene, to be used for Depth needed for convolutional neural networks behavior classifier needed for trained and iteration behavior Fast Classification and real-time behavioural analysis is rolled up The network model of product neural network behavior analyzer, so that the model can adapt to application scenarios, and it is continuous with using Improve adaptability and precision.
The present embodiment completes student of the present invention and learns filling smart profile system and method in school, which can To realize the intellectual monitoring of student's learning state, manpower teaching management cost is reduced, and find peel off row of the student in study For and reason, convenient for finding to and guide teaching in time, for promoted quality of instruction have significant effect and meaning.
Although the illustrative specific embodiment of the present invention is described above, in order to the technology of the art Personnel are it will be appreciated that the present invention, but the present invention is not limited only to the range of specific embodiment, to the common skill of the art For art personnel, as long as long as various change the attached claims limit and determine spirit and scope of the invention in, one The innovation and creation using present inventive concept are cut in the column of protection.

Claims (9)

1. a kind of student is in school learning state smart profile system, it is characterised in that: the student is in school learning state intelligence shelves Case system includes image data acquiring module M1, recognition of face locating module M2, the behavioural analysis mould using extensive control methods Block M3, learning state analysis module M6, filing processing module M7;
The student further includes scene behavior library module M10, archive of student module M8, institute in school learning state smart profile system It states archive of student module M8 and is also connected with and be provided with data aggregation module M9;
The filing processing module M7 pushes to archive of student module after handling the information package of learning state analysis module M6 M8;
The data aggregation module M9 is docked with teaching education administration system, realizes the convergence and application of data;
The scene behavior library module M10 includes Activity recognition basic data collection.
2. student according to claim 1 is in school learning state smart profile system, it is characterised in that: the behavioural analysis Module M3 is connected with difficult sample using module M4, and difficult sample is connected with abnormal scene processing module M5 using module;It is described Scene behavior library module M10 further includes the incoming new scene behavioral data collection of abnormal scene processing module M5.
3. a kind of student is in school learning state smart profile application method, it is characterised in that: the student is in school learning state intelligence Energy archives application method is based on student as claimed in claim 1 or 2 in school learning state smart profile system, the application method packet It includes:
Step 1, image data acquiring module M1 carry out image data acquiring, the facial image letter including acquisition student and teacher Breath, as the crucial recognition property of archives, and the real-time image information of acquisition student and teacher, the input as state analysis Information;
Step 2, recognition of face locating module M2 receive the real-time figure of image data acquiring module M1 incoming student and teacher As information, the face information in image is identified and positioned;
Step 3, behavioural analysis module M3 is received to be believed from the recognition of face locating module M2 real-time image information transmitted and personnel After breath, according to scene behavior library module M10, behavioural analysis is done using extensive control methods;
Step 4, peel off behavior screening, the behavior type that peels off evaluation, teacher of learning state analysis module M6 attend class behavior point It analyses, the team learning state at the behavioral reasons that peel off analysis certain moment of generation;
The information of learning state analysis module M6 is packaged and is handled by step 5, and is pushed to archive of student module M8, Archive of student module M8 is stored with personal information, total marks of the examination, history learning status information;
Step 6, the external interaction data of data aggregation module M9.
4. student according to claim 3 is in school learning state smart profile application method, it is characterised in that: step 2 packet It includes:
S201 carries out recognition of face and obtains the feature of facial image to the facial image register information of student and teacher, identification Characteristic results are denoted as Rc, and it is stored in archive of student module M8, it is registered for personnel's archives;
S202 carries out recognition of face and positioning obtains in field of view to the real-time image information of collected student and teacher All people's face information is identified and is positioned, and is identified and is denoted as R with the characteristic results of positioninglcAfter obtaining characteristic results, face is known Other locating module M2 calls the face register information in archive of student module M8, and comparison obtains all people person's information in the visual field;
The real-time image information and corresponding personal information of student and teacher are output to by S203, recognition of face locating module M2 Behavioural analysis module M3.
5. student according to claim 3 is in school learning state smart profile application method, it is characterised in that: step three guarantees It includes:
Step S301, member's fast grouping, behavioural analysis module M3 read realtime graphic picture from archive of student module M8 first In student's school grade for having recognized, if there is academic record, by marks sequencing gradient in archive of student module M8 It is divided into n group;It is one by member definitions all in video image if there is no academic record in archive of student module M8 A group;
Step 3012, image is decomposed by member, as unit of the group result in step 301, utilizes recognition of face and positioning side Method identifies the member of each group from image, by rapid edge-detection method, image is pressed member's Region Decomposition, generation is used for The decomposition image of behavioural analysis;
Step S302 constructs depth convolutional neural networks behavior analyzer, carries out typical behaviour analysis to s class.
6. student according to claim 5 is in school learning state smart profile application method, it is characterised in that: by quick Edge detection method includes:
Step 30121: the member region navigated to using recognition of face carries out rapid edge-detection as target area;
Step 30122: circulation carries out above-mentioned detection, and defining cycle-index is the number of members in image, and decomposition is independent decomposition Image, the input for behavior classifier;
Step 3013: the decomposition image in step 3012 being input to convolutional neural networks behavior classifier and is divided by behavior Class is divided into s class.
7. student according to claim 6 is in school learning state smart profile application method, it is characterised in that: the convolution The building of neural network behavior classifier the following steps are included:
Step 30131: the data set of building convolutional neural networks behavior classifier, using all big Activity recognition data sets as base Plinth data set, the user's scene behavioral data collection generated by abnormal scene processing module M5 is as growth data collection, basic number The data set of convolutional neural networks behavior classifier, the convolutional neural networks behavior point are collectively constituted according to collection and growth data collection The data set of class device is divided into training set, test set and verifying collection;
Step 30132: building is suitable for the convolutional neural networks of behavior classification, and defining the number of members in one group is m, the row of member To be divided into s class, s≤m selects a behavior as typical case at random from s class, does behavioural analysis.
8. student according to claim 7 is in school learning state smart profile application method, it is characterised in that: step S302 In include:
Step 3021, the sample data set of depth convolutional neural networks behavior analyzer is established, with big Activity recognition data set work For basic data set, the user's scene behavioral data collection generated by abnormal scene processing module M5 is as growth data collection, base Plinth data set and growth data collection collectively constitute the sample data set of depth convolutional neural networks behavior analyzer, the sample number Training set, test set and verifying collection are divided into according to collection;Depth convolutional neural networks are trained and are tested, until being met The depth convolutional neural networks behavior analyzer of pre-defined threshold requirement;
Step 3022: the image after decomposition being input in depth convolutional neural networks behavior analyzer, behavioural analysis knot is obtained Fruit, behavioural analysis result include that goal behavior particularly belongs to which kind of behavior and goal behavior can not parse;
Step 3023: for behavioural analysis the result is that the goal behavior that can not be parsed, defeated by corresponding goal behavior original image Enter to difficult specimen sample module M4.
9. student according to claim 8 is in school learning state smart profile application method, it is characterised in that: the difficulty The goal behavior that specimen sample module M4 can not parse behavioural analysis module M3 intercepts first n1 seconds and arrives rear n1 in its time domain The successive frame picture of second, is then pushed to abnormal scene processing module M5 for successive frame picture;
Successive frame image information is pushed to user, by user using event triggering method by the exception scene processing module M5 Artificial cognition is carried out, after typing differentiates result, by successive frame picture and differentiates that result is deposited into scene behavior library module M10, Convolutional neural networks behavior classifier and depth convolution to spread foundation Activity recognition database, in behavioural analysis module M3 The update iteration of neural network behavior analyzer network model.
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